From 90aa4204d03ffa5f2211be9bd6a49952871e2cfd Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Tue, 24 Sep 2024 06:02:37 +0200
Subject: [PATCH 01/48] Revert to using 64-bit precision.

---
 src/skillmodels/maximization_inputs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index aa885b1..b84c96e 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -15,7 +15,7 @@
 from skillmodels.process_debug_data import process_debug_data
 from skillmodels.process_model import process_model
 
-jax.config.update("jax_enable_x64", False)  # noqa: FBT003
+jax.config.update("jax_enable_x64", True)  # noqa: FBT003
 
 
 def get_maximization_inputs(model_dict, data):

From ceff7aadba5bdc9d6481608b09fc21eabf1fd58f Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Tue, 24 Sep 2024 18:33:39 +0200
Subject: [PATCH 02/48] Add function to check whether the model has
 investments.

---
 pixi.lock                              |  4 +--
 pyproject.toml                         | 13 ++++++++-
 src/skillmodels/maximization_inputs.py | 32 ++++++++++++++++++++++
 tests/test_maximization_inputs.py      | 37 +++++++++++++++++++++++++-
 4 files changed, 82 insertions(+), 4 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 3efe3f8..7af0515 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -14100,9 +14100,9 @@ packages:
   timestamp: 1620240338595
 - kind: pypi
   name: skillmodels
-  version: 0.1.dev258+g8312a84.d20240911
+  version: 0.0.24.dev236+g90aa420.d20240924
   path: .
-  sha256: 3202b313c9a1f6b5a8ae4fdbbe40c12ee62357da7f20f5f03628a3410a6a81d8
+  sha256: cbd6c9c7649c32bc746d1588af84b5bf8d4bc6200f63b7b8b29a4922995ac29d
   requires_dist:
   - dags
   - numpy
diff --git a/pyproject.toml b/pyproject.toml
index b505172..d0424a3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -276,9 +276,20 @@ extend-ignore = [
 "src/skillmodels/simulate_data.py" = ["D417"]
 "src/skillmodels/visualize_*.py" = ["BLE001", "D417"]
 "src/skillmodels/*_heatmap*.py" = ["D417"]
-"tests/*" = ["ARG001", "FBT003", "INP001", "PD002", "NPY002", "PTH123", "S506"]
 "**/*.ipynb" = ["B018", "T201", "E402", "PLR2004", "INP001", "PTH100"]
 "docs/**/*" = ["A001", "ERA001", "INP001", "PTH100", "PTH123", "S506"]
+"tests/*" = [
+    "ARG001",
+    "E712",
+    "E721",
+    "FBT003",
+    "INP001",
+    "PD002",
+    "PT011",
+    "NPY002",
+    "PTH123",
+    "S506"
+]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index b84c96e..bf05ae1 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -1,4 +1,5 @@
 import functools
+from typing import Any
 
 import jax
 import jax.numpy as jnp
@@ -18,6 +19,31 @@
 jax.config.update("jax_enable_x64", True)  # noqa: FBT003
 
 
+def model_has_investments(factors: dict[str, Any]) -> bool:
+    """Check whether there are any investment factors present."""
+    investments = pd.DataFrame(
+        [
+            {
+                "factor": f,
+                "is_investment": v.get("is_investment", False),
+                "is_correction": v.get("is_correction", False),
+            }
+            for f, v in factors.items()
+        ]
+    ).set_index("factor")
+    if (investments.dtypes != bool).any():  # noqa: E721
+        raise ValueError(
+            "If specified, 'is_investment' and 'is_correction' both need to be of type"
+            f"'bool', got:\n{investments}"
+        )
+    if (~investments["is_investment"] & investments["is_correction"]).any():
+        raise ValueError(
+            "A factor cannot be a correction and not an investment, got:\n"
+            f"{investments}"
+        )
+    return investments["is_investment"].any()
+
+
 def get_maximization_inputs(model_dict, data):
     """Create inputs for optimagic's maximize function.
 
@@ -42,8 +68,14 @@ def get_maximization_inputs(model_dict, data):
             model specification.
         params_template (pd.DataFrame): Parameter DataFrame with correct index and
             bounds but with empty value column.
+        data_aug (pd.DataFrame): DataFrame with augmented data. If model contains
+            investment factors, we double up the number of periods in order to add
+
+
 
     """
+    has_investments = model_has_investments(model_dict["factors"])
+
     model = process_model(model_dict)
     p_index = get_params_index(
         model["update_info"],
diff --git a/tests/test_maximization_inputs.py b/tests/test_maximization_inputs.py
index 0f901f2..282d8ee 100644
--- a/tests/test_maximization_inputs.py
+++ b/tests/test_maximization_inputs.py
@@ -1,7 +1,8 @@
 import jax.numpy as jnp
 import numpy as np
+import pytest
 
-from skillmodels.maximization_inputs import _to_numpy
+from skillmodels.maximization_inputs import _to_numpy, model_has_investments
 
 
 def test_to_numpy_with_dict():
@@ -19,3 +20,37 @@ def test_to_numpy_one_array():
 def test_to_numpy_one_float():
     calculated = _to_numpy(3.5)
     assert isinstance(calculated, float)
+
+
+def test_model_has_investments_not_specified():
+    factors = {"a": {}}
+    assert model_has_investments(factors) == False
+
+
+def test_model_has_investments_wrong_type():
+    factors = {"a": {"is_investment": 3}}
+    with pytest.raises(ValueError):
+        model_has_investments(factors)
+
+
+def test_model_has_investments_wrong_constellation():
+    factors = {"a": {"is_investment": False, "is_correction": True}}
+    with pytest.raises(ValueError):
+        model_has_investments(factors)
+
+
+def test_model_has_investments_indeed():
+    factors = {
+        "a": {"is_investment": True, "is_correction": False},
+        "b": {"is_investment": False, "is_correction": False},
+    }
+    assert model_has_investments(factors) == True
+
+
+def test_model_has_investments_and_correction():
+    factors = {
+        "a": {"is_investment": True, "is_correction": False},
+        "b": {"is_investment": False, "is_correction": False},
+        "c": {"is_investment": True, "is_correction": True},
+    }
+    assert model_has_investments(factors) == True

From 4527282b5fdeda461b0319a9d4ba64ac009f9658 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Wed, 25 Sep 2024 06:25:57 +0200
Subject: [PATCH 03/48] [WIP] Refactor: Include stuff in process_model /
 process_data.

---
 pixi.lock                              |   4 +-
 pyproject.toml                         |   4 +-
 src/skillmodels/maximization_inputs.py |  28 -----
 src/skillmodels/process_data.py        |  29 +++++
 src/skillmodels/process_model.py       |  75 ++++++++++--
 tests/test_maximization_inputs.py      |  37 +-----
 tests/test_process_model.py            | 160 ++++++++++++++++++++++++-
 7 files changed, 261 insertions(+), 76 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 7af0515..103d95b 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -14100,9 +14100,9 @@ packages:
   timestamp: 1620240338595
 - kind: pypi
   name: skillmodels
-  version: 0.0.24.dev236+g90aa420.d20240924
+  version: 0.0.24.dev237+gceff7aa.d20240925
   path: .
-  sha256: cbd6c9c7649c32bc746d1588af84b5bf8d4bc6200f63b7b8b29a4922995ac29d
+  sha256: 7408737e5806b681e4e330a503191f69bffa6ae324663f0a53341172ffff219a
   requires_dist:
   - dags
   - numpy
diff --git a/pyproject.toml b/pyproject.toml
index d0424a3..0713fd6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -218,6 +218,9 @@ extend-ignore = [
     # exception must not use an f-string literal
     "EM102",
 
+    # Boolean-typed positional arguments.
+    "FBT001",
+
     # Boolean default positional argument in function definition
     "FBT002",
 
@@ -281,7 +284,6 @@ extend-ignore = [
 "tests/*" = [
     "ARG001",
     "E712",
-    "E721",
     "FBT003",
     "INP001",
     "PD002",
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index bf05ae1..ce384c3 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -1,5 +1,4 @@
 import functools
-from typing import Any
 
 import jax
 import jax.numpy as jnp
@@ -19,31 +18,6 @@
 jax.config.update("jax_enable_x64", True)  # noqa: FBT003
 
 
-def model_has_investments(factors: dict[str, Any]) -> bool:
-    """Check whether there are any investment factors present."""
-    investments = pd.DataFrame(
-        [
-            {
-                "factor": f,
-                "is_investment": v.get("is_investment", False),
-                "is_correction": v.get("is_correction", False),
-            }
-            for f, v in factors.items()
-        ]
-    ).set_index("factor")
-    if (investments.dtypes != bool).any():  # noqa: E721
-        raise ValueError(
-            "If specified, 'is_investment' and 'is_correction' both need to be of type"
-            f"'bool', got:\n{investments}"
-        )
-    if (~investments["is_investment"] & investments["is_correction"]).any():
-        raise ValueError(
-            "A factor cannot be a correction and not an investment, got:\n"
-            f"{investments}"
-        )
-    return investments["is_investment"].any()
-
-
 def get_maximization_inputs(model_dict, data):
     """Create inputs for optimagic's maximize function.
 
@@ -74,8 +48,6 @@ def get_maximization_inputs(model_dict, data):
 
 
     """
-    has_investments = model_has_investments(model_dict["factors"])
-
     model = process_model(model_dict)
     p_index = get_params_index(
         model["update_info"],
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 6d9dba2..71146d5 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -1,4 +1,5 @@
 import warnings
+from typing import Any
 
 import jax.numpy as jnp
 import numpy as np
@@ -7,6 +8,34 @@
 from skillmodels.process_model import get_period_measurements
 
 
+def augment_data_for_investments(data: pd.DataFrame, model_dict: dict[str, Any]):
+    """Make room for endogenous investments by doubling up the periods.
+
+    Endogeneity of investments means that current states influence the
+
+    """
+    assert data.reset_index()["id_kid"].nunique() * 5 == data.shape[0]
+    assert set(data.reset_index()["trimester"].unique()) == set(
+        TRIM_AUG_TO_TRIMESTER.values()
+    )
+
+    out = pd.DataFrame()
+    for trim_aug, trimester in TRIM_AUG_TO_TRIMESTER.items():
+        meas_cols = sorted(
+            {
+                m
+                for f in model_dict["factors"].values()
+                for m in f["measurements"][trim_aug]
+            }
+        )
+        cols = model_dict["observed_factors"] + meas_cols
+        period_data = data.loc[(slice(None), trimester), cols].copy()
+        period_data["trim_aug"] = trim_aug
+        out = pd.concat([out, period_data])
+
+    out = out.reset_index().set_index(["id_kid", "trim_aug"]).sort_index()
+
+
 def process_data(df, labels, update_info, anchoring_info, purpose="estimation"):
     """Process the data for estimation.
 
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index f3ae09e..029390b 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -1,4 +1,5 @@
 from functools import partial
+from typing import Any, Literal
 
 import numpy as np
 import pandas as pd
@@ -38,14 +39,18 @@ def process_model(model_dict):
         loadings and intercepts for each factor. See :ref:`normalizations`.
 
     """
-    dims = get_dimensions(model_dict)
-    labels = _get_labels(model_dict, dims)
+    has_investments = get_has_investments(model_dict["factors"])
+    dims = get_dimensions(model_dict, has_investments)
+    labels = _get_labels(
+        model_dict=model_dict, has_investments=has_investments, dimensions=dims
+    )
     anchoring = _process_anchoring(model_dict)
     check_model(model_dict, labels, dims, anchoring)
     transition_info = _get_transition_info(model_dict, labels)
     labels["transition_names"] = list(transition_info["function_names"].values())
 
     processed = {
+        "has_investments": has_investments,
         "dimensions": dims,
         "labels": labels,
         "anchoring": anchoring,
@@ -57,11 +62,37 @@ def process_model(model_dict):
     return processed
 
 
-def get_dimensions(model_dict):
+def get_has_investments(factors: dict[str, Any]) -> bool:
+    """Return True if any investment factors are present."""
+    investments = pd.DataFrame(
+        [
+            {
+                "factor": f,
+                "is_investment": v.get("is_investment", False),
+                "is_correction": v.get("is_correction", False),
+            }
+            for f, v in factors.items()
+        ]
+    ).set_index("factor")
+    if (investments.dtypes != bool).any():  # noqa: E721
+        raise ValueError(
+            "If specified, 'is_investment' and 'is_correction' both need to be of type"
+            f"'bool', got:\n{investments}"
+        )
+    if (~investments["is_investment"] & investments["is_correction"]).any():
+        raise ValueError(
+            "A factor cannot be a correction and not an investment, got:\n"
+            f"{investments}"
+        )
+    return investments["is_investment"].any()
+
+
+def get_dimensions(model_dict, has_investments):
     """Extract the dimensions of the model.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
+        has_investments (bool): Whether investment factors are present.
 
     Returns:
         dict: Dimensional information like n_states, n_periods, n_controls,
@@ -69,24 +100,41 @@ def get_dimensions(model_dict):
 
     """
     all_n_periods = [len(d["measurements"]) for d in model_dict["factors"].values()]
+    n_periods_raw = max(all_n_periods)
+    n_periods = 2 * n_periods_raw if has_investments else n_periods_raw
 
     dims = {
         "n_latent_factors": len(model_dict["factors"]),
         "n_observed_factors": len(model_dict.get("observed_factors", [])),
-        "n_periods": max(all_n_periods),
-        # plus 1 for the constant
-        "n_controls": len(model_dict.get("controls", [])) + 1,
+        "n_periods": n_periods,
+        "n_periods_raw": n_periods_raw,
+        "n_controls": len(model_dict.get("controls", [])) + 1,  # plus 1: constant
         "n_mixtures": model_dict["estimation_options"].get("n_mixtures", 1),
     }
     dims["n_all_factors"] = dims["n_latent_factors"] + dims["n_observed_factors"]
     return dims
 
 
-def _get_labels(model_dict, dimensions):
+def _get_periods_to_periods_raw(
+    dimensions: dict[str, int], has_investments: bool
+) -> dict[int, int]:
+    """Return mapper of periods potentially augmented for investments to raw periods."""
+    periods = list(range(dimensions["n_periods"]))
+    return {p: p // 2 for p in periods} if has_investments else {p: p for p in periods}
+
+
+def _get_periods_to_period_types(
+    periods: list[int], has_investments: bool
+) -> dict[int, Literal["states", "investments"]]:
+    return {p: p // 2 for p in periods} if has_investments else {p: p for p in periods}
+
+
+def _get_labels(model_dict, has_investments, dimensions):
     """Extract labels of the model quantities.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
+        has_investments (bool): Whether investment factors are present.
         dimensions (dict): Dimensional information like n_states, n_periods, n_controls,
             n_mixtures. See :ref:`dimensions`.
 
@@ -95,13 +143,24 @@ def _get_labels(model_dict, dimensions):
         factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     """
+    if has_investments and "stagemap" in model_dict:
+        raise ValueError("Stages currently not supported when investments are present.")
+
     stagemap = model_dict.get("stagemap", list(range(dimensions["n_periods"] - 1)))
 
+    periods_to_periods_raw = _get_periods_to_periods_raw(dimensions, has_investments)
+    periods_to_period_types = _get_periods_to_period_types(
+        list(periods_to_periods_raw.keys()), has_investments
+    )
+
     labels = {
         "latent_factors": list(model_dict["factors"]),
         "observed_factors": list(model_dict.get("observed_factors", [])),
         "controls": ["constant", *list(model_dict.get("controls", []))],
-        "periods": list(range(dimensions["n_periods"])),
+        "periods": list(periods_to_periods_raw.keys()),
+        "periods_raw": sorted(set(periods_to_periods_raw.values())),
+        "periods_to_periods_raw": periods_to_periods_raw,
+        "periods_to_period_types": periods_to_period_types,
         "stagemap": stagemap,
         "stages": sorted(np.unique(stagemap)),
     }
diff --git a/tests/test_maximization_inputs.py b/tests/test_maximization_inputs.py
index 282d8ee..0f901f2 100644
--- a/tests/test_maximization_inputs.py
+++ b/tests/test_maximization_inputs.py
@@ -1,8 +1,7 @@
 import jax.numpy as jnp
 import numpy as np
-import pytest
 
-from skillmodels.maximization_inputs import _to_numpy, model_has_investments
+from skillmodels.maximization_inputs import _to_numpy
 
 
 def test_to_numpy_with_dict():
@@ -20,37 +19,3 @@ def test_to_numpy_one_array():
 def test_to_numpy_one_float():
     calculated = _to_numpy(3.5)
     assert isinstance(calculated, float)
-
-
-def test_model_has_investments_not_specified():
-    factors = {"a": {}}
-    assert model_has_investments(factors) == False
-
-
-def test_model_has_investments_wrong_type():
-    factors = {"a": {"is_investment": 3}}
-    with pytest.raises(ValueError):
-        model_has_investments(factors)
-
-
-def test_model_has_investments_wrong_constellation():
-    factors = {"a": {"is_investment": False, "is_correction": True}}
-    with pytest.raises(ValueError):
-        model_has_investments(factors)
-
-
-def test_model_has_investments_indeed():
-    factors = {
-        "a": {"is_investment": True, "is_correction": False},
-        "b": {"is_investment": False, "is_correction": False},
-    }
-    assert model_has_investments(factors) == True
-
-
-def test_model_has_investments_and_correction():
-    factors = {
-        "a": {"is_investment": True, "is_correction": False},
-        "b": {"is_investment": False, "is_correction": False},
-        "c": {"is_investment": True, "is_correction": True},
-    }
-    assert model_has_investments(factors) == True
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index c8ea18f..6d654cb 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -6,7 +6,7 @@
 import yaml
 from pandas.testing import assert_frame_equal
 
-from skillmodels.process_model import process_model
+from skillmodels.process_model import get_has_investments, process_model
 
 # ======================================================================================
 # Integration test with model2 from the replication files of CHS2010
@@ -23,6 +23,10 @@ def model2():
     return model_dict
 
 
+def test_has_investments(model2):
+    assert process_model(model2)["has_investments"] == False
+
+
 def test_dimensions(model2):
     res = process_model(model2)["dimensions"]
     assert res["n_latent_factors"] == 3
@@ -115,3 +119,157 @@ def test_normalizations(model2):
     res = process_model(model2)["normalizations"]
 
     assert res == expected
+
+
+# ======================================================================================
+# Augment model2 with investments
+# ======================================================================================
+
+
+@pytest.fixture
+def model2_inv():
+    with open(TEST_DIR / "model2.yaml") as y:
+        model_dict = yaml.load(y, Loader=yaml.FullLoader)
+    # Set fac3 to be an investment
+    model_dict["factors"]["fac3"]["is_investment"] = True
+    del model_dict["stagemap"]
+    return model_dict
+
+
+def test_with_inv_has_investments(model2_inv):
+    assert process_model(model2_inv)["has_investments"] == True
+
+
+def test_with_inv_dimensions(model2_inv):
+    res = process_model(model2_inv)["dimensions"]
+    assert res["n_latent_factors"] == 3
+    assert res["n_observed_factors"] == 0
+    assert res["n_all_factors"] == 3
+    assert res["n_periods"] == 16
+    assert res["n_periods_raw"] == 8
+    assert res["n_controls"] == 2
+    assert res["n_mixtures"] == 1
+
+
+def test_with_inv_labels(model2_inv):
+    res = process_model(model2_inv)["labels"]
+    assert res["latent_factors"] == ["fac1", "fac2", "fac3"]
+    assert res["observed_factors"] == []
+    assert res["all_factors"] == ["fac1", "fac2", "fac3"]
+    assert res["controls"] == ["constant", "x1"]
+    assert res["periods"] == list(range(16))
+    assert res["periods_raw"] == [0, 1, 2, 3, 4, 5, 6, 7]
+    assert res["stagemap"] == list(range(15))
+    assert res["stages"] == list(range(15))
+
+
+def test_with_inv_estimation_options(model2_inv):
+    res = process_model(model2_inv)["estimation_options"]
+    assert res["sigma_points_scale"] == 2
+    assert res["robust_bounds"]
+    assert res["bounds_distance"] == 0.001
+
+
+def test_with_inv_anchoring(model2_inv):
+    res = process_model(model2_inv)["anchoring"]
+    assert res["outcomes"] == {"fac1": "Q1"}
+    assert res["factors"] == ["fac1"]
+    assert res["free_controls"]
+    assert res["free_constant"]
+    assert res["free_loadings"]
+
+
+def test_with_inv_transition_info(model2_inv):
+    res = process_model(model2_inv)["transition_info"]
+
+    assert isinstance(res, dict)
+    assert callable(res["func"])
+
+    assert list(inspect.signature(res["func"]).parameters) == ["params", "states"]
+
+
+def test_with_inv_update_info(model2_inv):
+    res = process_model(model2_inv)["update_info"]
+    test_dir = Path(__file__).parent.resolve()
+    expected = pd.read_csv(
+        test_dir / "model2_correct_update_info.csv",
+        index_col=["period", "variable"],
+    )
+    assert_frame_equal(res, expected)
+
+
+def test_with_inv_normalizations(model2_inv):
+    expected = {
+        "fac1": {
+            "loadings": [
+                {"y1": 1},
+                {"y1": 1},
+                {"y1": 1},
+                {"y1": 1},
+                {"y1": 1},
+                {"y1": 1},
+                {"y1": 1},
+                {"y1": 1},
+            ],
+            "intercepts": [{}, {}, {}, {}, {}, {}, {}, {}],
+        },
+        "fac2": {
+            "loadings": [
+                {"y4": 1},
+                {"y4": 1},
+                {"y4": 1},
+                {"y4": 1},
+                {"y4": 1},
+                {"y4": 1},
+                {"y4": 1},
+                {"y4": 1},
+            ],
+            "intercepts": [{}, {}, {}, {}, {}, {}, {}, {}],
+        },
+        "fac3": {
+            "loadings": [{"y7": 1}, {}, {}, {}, {}, {}, {}, {}],
+            "intercepts": [{}, {}, {}, {}, {}, {}, {}, {}],
+        },
+    }
+    res = process_model(model2_inv)["normalizations"]
+
+    assert res == expected
+
+
+# ======================================================================================
+# Unit tests
+# ======================================================================================
+
+
+def test_model_has_investments_not_specified():
+    factors = {"a": {}}
+    assert get_has_investments(factors) == False
+
+
+def test_get_has_investments_wrong_type():
+    factors = {"a": {"is_investment": 3}}
+    with pytest.raises(ValueError):
+        get_has_investments(factors)
+
+
+def test_get_has_investments_wrong_constellation():
+    factors = {"a": {"is_investment": False, "is_correction": True}}
+    with pytest.raises(ValueError):
+        get_has_investments(factors)
+
+
+def test_get_has_investments_indeed():
+    factors = {
+        "a": {"is_investment": True, "is_correction": False},
+        "b": {"is_investment": False, "is_correction": False},
+    }
+    assert get_has_investments(factors) == True
+
+
+def test_get_has_investments_and_correction():
+    factors = {
+        "a": {"is_investment": True, "is_correction": False},
+        "b": {"is_investment": False, "is_correction": False},
+        "c": {"is_investment": True, "is_correction": True},
+    }
+    assert get_has_investments(factors) == True

From 1171b7e4fad340dde39159d2bc06d7d2dcdfc59c Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Wed, 25 Sep 2024 08:11:43 +0200
Subject: [PATCH 04/48] Backup commit before removing fill_list() feature.

---
 src/skillmodels/process_model.py | 45 ++++++++++++++++++++++++++++++--
 1 file changed, 43 insertions(+), 2 deletions(-)

diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 029390b..d7f5921 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -1,3 +1,4 @@
+from copy import deepcopy
 from functools import partial
 from typing import Any, Literal
 
@@ -46,7 +47,11 @@ def process_model(model_dict):
     )
     anchoring = _process_anchoring(model_dict)
     check_model(model_dict, labels, dims, anchoring)
-    transition_info = _get_transition_info(model_dict, labels)
+    if has_investments:
+        model_dict_aug = _augment_periods_for_investments(model_dict, labels)
+    else:
+        model_dict_aug = model_dict
+    transition_info = _get_transition_info(model_dict_aug, labels)
     labels["transition_names"] = list(transition_info["function_names"].values())
 
     processed = {
@@ -126,7 +131,12 @@ def _get_periods_to_periods_raw(
 def _get_periods_to_period_types(
     periods: list[int], has_investments: bool
 ) -> dict[int, Literal["states", "investments"]]:
-    return {p: p // 2 for p in periods} if has_investments else {p: p for p in periods}
+    return {
+        p: ("states" if p % 2 == 0 else "investments")
+        if has_investments
+        else {p: "states"}
+        for p in periods
+    }
 
 
 def _get_labels(model_dict, has_investments, dimensions):
@@ -225,6 +235,37 @@ def _process_anchoring(model_dict):
     return anchinfo
 
 
+def _insert_empty_elements_into_list(old, insert_at_modulo, to_insert, p_to_p_raw):
+    return [
+        to_insert if p % 2 == insert_at_modulo else old[p_raw]
+        for p, p_raw in p_to_p_raw.items()
+    ]
+
+
+def _augment_periods_for_investments(
+    model_dict: dict[str, Any], labels: dict[str, Any]
+) -> dict[str, Any]:
+    """Insert periods without measurements / normalisations if investments are present.
+
+    Args:
+        model_dict: The model specification. See: :ref:`model_specs`
+        labels: Dictionary with information about periods etc.
+
+    Returns:
+        Model dictionary with twice the amount of periods
+
+    """
+    aug = deepcopy(model_dict)
+    for fac, v in model_dict["factors"].items():
+        insert_at_modulo = 0 if v.get("is_investment", False) else 1
+        aug["factors"][fac]["measurements"] = _insert_empty_elements_into_list(
+            old=v["measurements"],
+            insert_at_modulo=insert_at_modulo,
+            to_insert=[],
+            p_to_p_raw=labels["periods_to_periods_raw"],
+        )
+
+
 def _get_transition_info(model_dict, labels):
     """Collect information about transition functions."""
     func_list, param_names = [], []

From 58e5732cf8544643317ea9c5e8122a4e8ae0fdc4 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Wed, 25 Sep 2024 14:47:17 +0200
Subject: [PATCH 05/48] Fix tests, model processing seems to work with
 additional periods now. Missing data and constraints.

---
 docs/source/how_to_guides/model_specs.rst     |  12 +-
 src/skillmodels/process_model.py              |  72 ++++++++--
 src/skillmodels/utilities.py                  |   7 +-
 tests/model2.yaml                             |  14 ++
 tests/model2_with_inv_correct_update_info.csv |  52 +++++++
 tests/test_process_model.py                   | 131 ++++++++++++++++--
 tests/test_utilities.py                       |   1 +
 7 files changed, 252 insertions(+), 37 deletions(-)
 create mode 100644 tests/model2_with_inv_correct_update_info.csv

diff --git a/docs/source/how_to_guides/model_specs.rst b/docs/source/how_to_guides/model_specs.rst
index d043760..ecd7ff0 100644
--- a/docs/source/how_to_guides/model_specs.rst
+++ b/docs/source/how_to_guides/model_specs.rst
@@ -96,14 +96,8 @@ Any python string is possible as factor name. The values are dictionaries with t
 entries:
 
 - measurements: A nested list that is as long as the number of periods of the model.
-  Each sublist contains the names of the measurements in that period. If A factor has
-  no measurements in a period, it has to be an empty list. If a factor only has
-  measurements up to a certain period you can leave out the empty lists at the end. In
-  the example this is done for factor 3. Note that even in that case, the measurements
-  have to be specified as nested list. If a factor only starts having measurements in
-  some period, you still have to specify the empty lists for all periods before that
-  period.
-
+  Each sublist contains the names of the measurements in that period. If a factor has
+  no measurements in a period, it has to be an empty list.
 
 - normalizations: This entry is optional. It is a dictionary that can have the keys
   ``"loadings"`` and ``"intercepts"``. The values are lists of dictionaries. The list
@@ -114,7 +108,7 @@ entries:
 - transition_equation: A string with the name of a pre-implemented transition equation
   or a custom transition equation. Pre-implemented transition equations are
   linear, log_ces (in the known location and scale version), constant and translog.
-  The example model dictionary only uses pre-implement transition functions.
+  The example model dictionary only uses pre-implemented transition functions.
 
   To see how to use custom transition functions, assume that the yaml file shown above
   has been loaded into a python dictionary called ``model`` and look at the following
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index d7f5921..db6129d 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -45,10 +45,14 @@ def process_model(model_dict):
     labels = _get_labels(
         model_dict=model_dict, has_investments=has_investments, dimensions=dims
     )
-    anchoring = _process_anchoring(model_dict)
+    anchoring = _process_anchoring(model_dict, has_investments)
     check_model(model_dict, labels, dims, anchoring)
     if has_investments:
-        model_dict_aug = _augment_periods_for_investments(model_dict, labels)
+        model_dict_aug = _augment_periods_for_investments(
+            model_dict=model_dict,
+            dimensions=dims,
+            labels=labels,
+        )
     else:
         model_dict_aug = model_dict
     transition_info = _get_transition_info(model_dict_aug, labels)
@@ -59,10 +63,10 @@ def process_model(model_dict):
         "dimensions": dims,
         "labels": labels,
         "anchoring": anchoring,
-        "estimation_options": _process_estimation_options(model_dict),
+        "estimation_options": _process_estimation_options(model_dict_aug),
         "transition_info": transition_info,
-        "update_info": _get_update_info(model_dict, dims, labels, anchoring),
-        "normalizations": _process_normalizations(model_dict, dims, labels),
+        "update_info": _get_update_info(model_dict_aug, dims, labels, anchoring),
+        "normalizations": _process_normalizations(model_dict_aug, dims, labels),
     }
     return processed
 
@@ -207,16 +211,20 @@ def _process_estimation_options(model_dict):
     return default_options
 
 
-def _process_anchoring(model_dict):
+def _process_anchoring(model_dict, has_investments):
     """Process the specification that governs how latent factors are anchored.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
+        has_investments (bool): Whether the model has any investments.
 
     Returns:
         dict: Dictionary with information about anchoring. See :ref:`anchoring`
 
     """
+    if "anchoring" in model_dict and has_investments:
+        raise ValueError("anchoring is not supported when investments are present.")
+
     anchinfo = {
         "anchoring": False,
         "outcomes": {},
@@ -243,13 +251,16 @@ def _insert_empty_elements_into_list(old, insert_at_modulo, to_insert, p_to_p_ra
 
 
 def _augment_periods_for_investments(
-    model_dict: dict[str, Any], labels: dict[str, Any]
+    model_dict: dict[str, Any], dimensions: dict[str, Any], labels: dict[str, Any]
 ) -> dict[str, Any]:
     """Insert periods without measurements / normalisations if investments are present.
 
     Args:
         model_dict: The model specification. See: :ref:`model_specs`
-        labels: Dictionary with information about periods etc.
+        dimensions (dict): Dimensional information like n_states, n_periods, n_controls,
+            n_mixtures. See :ref:`dimensions`.
+        labels (dict): Dict of lists with labels for the model quantities like
+            factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     Returns:
         Model dictionary with twice the amount of periods
@@ -258,6 +269,13 @@ def _augment_periods_for_investments(
     aug = deepcopy(model_dict)
     for fac, v in model_dict["factors"].items():
         insert_at_modulo = 0 if v.get("is_investment", False) else 1
+
+        # Insert empty elements into measurements when we do not have those.
+        if len(v["measurements"]) != dimensions["n_periods_raw"]:
+            raise ValueError(
+                "Measurements must be of length `n_periods_raw`, "
+                f"got {v['measurements']} for {fac}"
+            )
         aug["factors"][fac]["measurements"] = _insert_empty_elements_into_list(
             old=v["measurements"],
             insert_at_modulo=insert_at_modulo,
@@ -265,6 +283,23 @@ def _augment_periods_for_investments(
             p_to_p_raw=labels["periods_to_periods_raw"],
         )
 
+        # Insert empty elements into normalizations when we do not have those.
+        for norm_type, normalizations in v.get("normalizations", {}).items():
+            if not len(normalizations) == dimensions["n_periods_raw"]:
+                raise ValueError(
+                    "Normalizations must be lists of length `n_periods`, "
+                    f"got {normalizations} for {fac}['normalizations']['{norm_type}']"
+                )
+            aug["factors"][fac]["normalizations"][norm_type] = (
+                _insert_empty_elements_into_list(
+                    old=normalizations,
+                    insert_at_modulo=insert_at_modulo,
+                    to_insert={},
+                    p_to_p_raw=labels["periods_to_periods_raw"],
+                )
+            )
+    return aug
+
 
 def _get_transition_info(model_dict, labels):
     """Collect information about transition functions."""
@@ -355,11 +390,12 @@ def _get_update_info(model_dict, dimensions, labels, anchoring_info):
 
     measurements = {}
     for factor in labels["latent_factors"]:
-        measurements[factor] = fill_list(
-            model_dict["factors"][factor]["measurements"],
-            [],
-            dimensions["n_periods"],
-        )
+        measurements[factor] = model_dict["factors"][factor]["measurements"]
+        if len(measurements[factor]) != dimensions["n_periods"]:
+            raise ValueError(
+                "Measurements must be of length `n_periods`, "
+                f"got {measurements[factor]} for {factor}"
+            )
 
     for period in labels["periods"]:
         for factor in labels["latent_factors"]:
@@ -397,8 +433,14 @@ def _process_normalizations(model_dict, dimensions, labels):
         normalizations[factor] = {}
         norminfo = model_dict["factors"][factor].get("normalizations", {})
         for norm_type in ["loadings", "intercepts"]:
-            candidate = norminfo.get(norm_type, [])
-            candidate = fill_list(candidate, {}, dimensions["n_periods"])
+            candidate = norminfo.get(
+                norm_type, [{} for _ in range(dimensions["n_periods"])]
+            )
+            if not len(candidate) == dimensions["n_periods"]:
+                raise ValueError(
+                    "Normalizations must be of length `n_periods`, "
+                    f"got {norminfo} for {factor}['{norm_type}']"
+                )
             normalizations[factor][norm_type] = candidate
 
     return normalizations
diff --git a/src/skillmodels/utilities.py b/src/skillmodels/utilities.py
index a811f7a..f0720ef 100644
--- a/src/skillmodels/utilities.py
+++ b/src/skillmodels/utilities.py
@@ -5,7 +5,7 @@
 import pandas as pd
 
 from skillmodels.params_index import get_params_index
-from skillmodels.process_model import get_dimensions, process_model
+from skillmodels.process_model import get_dimensions, get_has_investments, process_model
 
 
 def extract_factors(factors, model_dict, params=None):
@@ -81,6 +81,9 @@ def remove_factors(factors, model_dict, params=None):
         pandas.DataFrame: The reduced parameter DataFrame (only if params is not None)
 
     """
+    # We need this for the full model when investments are present.
+    has_investments = get_has_investments(model_dict["factors"])
+
     out = deepcopy(model_dict)
 
     out["factors"] = _remove_from_dict(out["factors"], factors)
@@ -95,7 +98,7 @@ def remove_factors(factors, model_dict, params=None):
             out = _remove_from_dict(out, "anchoring")
 
     # Remove periods if necessary
-    new_n_periods = get_dimensions(out)["n_periods"]
+    new_n_periods = get_dimensions(out, has_investments)["n_periods"]
     out = reduce_n_periods(out, new_n_periods)
 
     if params is not None:
diff --git a/tests/model2.yaml b/tests/model2.yaml
index 5cfd872..3b80ca3 100644
--- a/tests/model2.yaml
+++ b/tests/model2.yaml
@@ -45,10 +45,24 @@ factors:
   fac3:
     measurements:
       - [y7, y8, y9]
+      - []
+      - []
+      - []
+      - []
+      - []
+      - []
+      - []
     transition_function: constant
     normalizations:
       loadings:
         - {y7: 1}
+        - {}
+        - {}
+        - {}
+        - {}
+        - {}
+        - {}
+        - {}
 anchoring:
   outcomes: {fac1: Q1}
   free_controls: true
diff --git a/tests/model2_with_inv_correct_update_info.csv b/tests/model2_with_inv_correct_update_info.csv
new file mode 100644
index 0000000..3ca7335
--- /dev/null
+++ b/tests/model2_with_inv_correct_update_info.csv
@@ -0,0 +1,52 @@
+period,variable,fac1,fac2,fac3,purpose
+0,y1,True,False,False,measurement
+0,y2,True,False,False,measurement
+0,y3,True,False,False,measurement
+0,y4,False,True,False,measurement
+0,y5,False,True,False,measurement
+0,y6,False,True,False,measurement
+1,y7,False,False,True,measurement
+1,y8,False,False,True,measurement
+1,y9,False,False,True,measurement
+2,y1,True,False,False,measurement
+2,y2,True,False,False,measurement
+2,y3,True,False,False,measurement
+2,y4,False,True,False,measurement
+2,y5,False,True,False,measurement
+2,y6,False,True,False,measurement
+4,y1,True,False,False,measurement
+4,y2,True,False,False,measurement
+4,y3,True,False,False,measurement
+4,y4,False,True,False,measurement
+4,y5,False,True,False,measurement
+4,y6,False,True,False,measurement
+6,y1,True,False,False,measurement
+6,y2,True,False,False,measurement
+6,y3,True,False,False,measurement
+6,y4,False,True,False,measurement
+6,y5,False,True,False,measurement
+6,y6,False,True,False,measurement
+8,y1,True,False,False,measurement
+8,y2,True,False,False,measurement
+8,y3,True,False,False,measurement
+8,y4,False,True,False,measurement
+8,y5,False,True,False,measurement
+8,y6,False,True,False,measurement
+10,y1,True,False,False,measurement
+10,y2,True,False,False,measurement
+10,y3,True,False,False,measurement
+10,y4,False,True,False,measurement
+10,y5,False,True,False,measurement
+10,y6,False,True,False,measurement
+12,y1,True,False,False,measurement
+12,y2,True,False,False,measurement
+12,y3,True,False,False,measurement
+12,y4,False,True,False,measurement
+12,y5,False,True,False,measurement
+12,y6,False,True,False,measurement
+14,y1,True,False,False,measurement
+14,y2,True,False,False,measurement
+14,y3,True,False,False,measurement
+14,y4,False,True,False,measurement
+14,y5,False,True,False,measurement
+14,y6,False,True,False,measurement
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index 6d654cb..b8cacb9 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -126,6 +126,30 @@ def test_normalizations(model2):
 # ======================================================================================
 
 
+def test_anchoring_and_investments_conflict():
+    with open(TEST_DIR / "model2.yaml") as y:
+        model_dict = yaml.load(y, Loader=yaml.FullLoader)
+    # Set fac3 to be an investment
+    model_dict["factors"]["fac3"]["is_investment"] = True
+    del model_dict["stagemap"]
+    with pytest.raises(
+        ValueError, match="anchoring is not supported when investments are present."
+    ):
+        process_model(model_dict)
+
+
+def test_stages_and_investments_conflict():
+    with open(TEST_DIR / "model2.yaml") as y:
+        model_dict = yaml.load(y, Loader=yaml.FullLoader)
+    # Set fac3 to be an investment
+    model_dict["factors"]["fac3"]["is_investment"] = True
+    del model_dict["anchoring"]
+    with pytest.raises(
+        ValueError, match="Stages currently not supported when investments are present"
+    ):
+        process_model(model_dict)
+
+
 @pytest.fixture
 def model2_inv():
     with open(TEST_DIR / "model2.yaml") as y:
@@ -133,6 +157,7 @@ def model2_inv():
     # Set fac3 to be an investment
     model_dict["factors"]["fac3"]["is_investment"] = True
     del model_dict["stagemap"]
+    del model_dict["anchoring"]
     return model_dict
 
 
@@ -170,13 +195,13 @@ def test_with_inv_estimation_options(model2_inv):
     assert res["bounds_distance"] == 0.001
 
 
-def test_with_inv_anchoring(model2_inv):
+def test_with_inv_anchoring_is_empty(model2_inv):
     res = process_model(model2_inv)["anchoring"]
-    assert res["outcomes"] == {"fac1": "Q1"}
-    assert res["factors"] == ["fac1"]
-    assert res["free_controls"]
-    assert res["free_constant"]
-    assert res["free_loadings"]
+    assert res["outcomes"] == {}
+    assert res["factors"] == []
+    assert res["free_controls"] is False
+    assert res["free_constant"] is False
+    assert res["free_loadings"] is False
 
 
 def test_with_inv_transition_info(model2_inv):
@@ -192,7 +217,7 @@ def test_with_inv_update_info(model2_inv):
     res = process_model(model2_inv)["update_info"]
     test_dir = Path(__file__).parent.resolve()
     expected = pd.read_csv(
-        test_dir / "model2_correct_update_info.csv",
+        test_dir / "model2_with_inv_correct_update_info.csv",
         index_col=["period", "variable"],
     )
     assert_frame_equal(res, expected)
@@ -203,32 +228,116 @@ def test_with_inv_normalizations(model2_inv):
         "fac1": {
             "loadings": [
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
                 {"y1": 1},
+                {},
+            ],
+            "intercepts": [
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
             ],
-            "intercepts": [{}, {}, {}, {}, {}, {}, {}, {}],
         },
         "fac2": {
             "loadings": [
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
                 {"y4": 1},
+                {},
+            ],
+            "intercepts": [
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
             ],
-            "intercepts": [{}, {}, {}, {}, {}, {}, {}, {}],
         },
         "fac3": {
-            "loadings": [{"y7": 1}, {}, {}, {}, {}, {}, {}, {}],
-            "intercepts": [{}, {}, {}, {}, {}, {}, {}, {}],
+            "loadings": [
+                {},
+                {"y7": 1},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+            ],
+            "intercepts": [
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+                {},
+            ],
         },
     }
     res = process_model(model2_inv)["normalizations"]
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index 22b468d..a35f700 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -147,6 +147,7 @@ def test_reduce_params_via_extract_factors(model2):
             ("meas_sds", 0, "y7", "-"),
             ("meas_sds", 0, "y8", "-"),
             ("meas_sds", 0, "y9", "-"),
+            ("shock_sds", 0, "fac3", "-"),
             ("initial_states", 0, "mixture_0", "fac3"),
             ("mixture_weights", 0, "mixture_0", "-"),
             ("initial_cholcovs", 0, "mixture_0", "fac3-fac3"),

From 30bcd923db4c87b9b68bbac30b73d4621755c518 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Wed, 25 Sep 2024 14:51:31 +0200
Subject: [PATCH 06/48] Remove fill_list().

---
 src/skillmodels/process_model.py | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index db6129d..4af05ff 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -446,22 +446,6 @@ def _process_normalizations(model_dict, dimensions, labels):
     return normalizations
 
 
-def fill_list(short_list, fill_value, length):
-    """Extend a list to specified length by filling it with the fill_value.
-
-    Examples:
-    >>> fill_list(["a"], "b", 3)
-    ['a', 'b', 'b']
-
-    """
-    res = list(short_list)
-    diff = length - len(short_list)
-    assert diff >= 0, "short_list has to be shorter than length."
-    if diff >= 1:
-        res += [fill_value] * diff
-    return res
-
-
 def get_period_measurements(update_info, period):
     if period in update_info.index:
         measurements = list(update_info.loc[period].index)

From 24e3527658646c1bcffc5cdd18cd540b88fa3e62 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Wed, 25 Sep 2024 15:41:28 +0200
Subject: [PATCH 07/48] [WIP] re-write process_data s.t. it returns a
 dictionary. Not working yet with investments.

---
 src/skillmodels/maximization_inputs.py        | 18 ++++----
 src/skillmodels/process_data.py               | 41 +++++++++++--------
 src/skillmodels/simulate_data.py              | 13 +++---
 .../visualize_transition_equations.py         |  5 ++-
 4 files changed, 45 insertions(+), 32 deletions(-)

diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index ce384c3..21ac67d 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -62,11 +62,13 @@ def get_maximization_inputs(model_dict, data):
         model["labels"],
         model["anchoring"],
     )
-    measurements, controls, observed_factors = process_data(
-        data,
-        model["labels"],
-        model["update_info"],
-        model["anchoring"],
+    processed_data = process_data(
+        df=data,
+        has_investments=model["has_investments"],
+        labels=model["labels"],
+        update_info=model["update_info"],
+        anchoring_info=model["anchoring"],
+        purpose="estimation",
     )
 
     sigma_scaling_factor, sigma_weights = calculate_sigma_scaling_factor_and_weights(
@@ -88,9 +90,9 @@ def get_maximization_inputs(model_dict, data):
         partialed_loglikes[n] = _partial_some_log_likelihood(
             fun=fun,
             parsing_info=parsing_info,
-            measurements=measurements,
-            controls=controls,
-            observed_factors=observed_factors,
+            measurements=processed_data["measurements"],
+            controls=processed_data["controls"],
+            observed_factors=processed_data["observed_factors"],
             model=model,
             sigma_weights=sigma_weights,
             sigma_scaling_factor=sigma_scaling_factor,
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 71146d5..8a210fc 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -8,12 +8,13 @@
 from skillmodels.process_model import get_period_measurements
 
 
-def augment_data_for_investments(data: pd.DataFrame, model_dict: dict[str, Any]):
+def _augment_data_for_investments(data: pd.DataFrame, labels: dict[str, Any]):
     """Make room for endogenous investments by doubling up the periods.
 
     Endogeneity of investments means that current states influence the
 
     """
+    # Make sure datset is balanced
     assert data.reset_index()["id_kid"].nunique() * 5 == data.shape[0]
     assert set(data.reset_index()["trimester"].unique()) == set(
         TRIM_AUG_TO_TRIMESTER.values()
@@ -36,12 +37,15 @@ def augment_data_for_investments(data: pd.DataFrame, model_dict: dict[str, Any])
     out = out.reset_index().set_index(["id_kid", "trim_aug"]).sort_index()
 
 
-def process_data(df, labels, update_info, anchoring_info, purpose="estimation"):
+def process_data(
+    df, has_investments, labels, update_info, anchoring_info, purpose="estimation"
+):
     """Process the data for estimation.
 
     Args:
         df (DataFrame): panel dataset in long format. It has a MultiIndex
             where the first level indicates the period and the second the individual.
+        has_investments (bool):
         labels (dict): Dict of lists with labels for the model quantities like
             factors, periods, controls, stagemap and stages. See :ref:`labels`
         update_info (pandas.DataFrame): DataFrame with one row per Kalman update needed
@@ -50,30 +54,33 @@ def process_data(df, labels, update_info, anchoring_info, purpose="estimation"):
         purpose (Literal["estimation", "anything"]): Whether the data is used for
             estimation (default, includes measurement data) or not.
 
-    Returns:
-        meas_data (jax.numpy.array): Array of shape (n_updates, n_obs) with data on
-            observed measurements. NaN if the measurement was not observed.
-        control_data (jax.numpy.array): Array of shape (n_periods, n_obs, n_controls)
-            with observed control variables for the measurement equations.
-        observed_factors (jax.numpy.array): Array of shape (n_periods, n_obs,
+    Returns a dictionary with keys:
+        controls (jax.numpy.array): Array of shape (n_updates, n_obs) with data on
+            observed measurements. NaN if the measurement was not observed. Only
+            returned if estimation==True
+        observed_factors (jax.numpy.array): Array of shape
+            (n_periods, n_obs, n_controls) with observed control variables for the
+            measurement equations.
+        measurements (jax.numpy.array): Array of shape (n_periods, n_obs,
             n_observed_factors) with data on the observed factors.
 
     """
-    df = pre_process_data(df, labels["periods"])
+    df = pre_process_data(df, labels["periods_raw"])
     df["constant"] = 1
-    df = _add_copies_of_anchoring_outcome(df, anchoring_info)
+    out = {}
+
+    if has_investments:
+        df = _augment_data_for_investments(df, labels)
+    else:
+        df = _add_copies_of_anchoring_outcome(df, anchoring_info)
     _check_data(df, update_info, labels, purpose=purpose)
     n_obs = int(len(df) / len(labels["periods"]))
     df = _handle_controls_with_missings(df, labels["controls"], update_info)
-    if purpose == "estimation":
-        meas_data = _generate_measurements_array(df, update_info, n_obs)
-    control_data = _generate_controls_array(df, labels, n_obs)
-    observed_data = _generate_observed_factor_array(df, labels, n_obs)
+    out["controls"] = _generate_controls_array(df, labels, n_obs)
+    out["observed_factors"] = _generate_observed_factor_array(df, labels, n_obs)
 
     if purpose == "estimation":
-        out = (meas_data, control_data, observed_data)
-    else:
-        out = (control_data, observed_data)
+        out["measurements"] = _generate_measurements_array(df, update_info, n_obs)
     return out
 
 
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index 56cfd75..59c2028 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -50,13 +50,16 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         raise ValueError("To simulate a model with controls, data cannot be None.")
 
     if data is not None:
-        control_data, observed_data = process_data(
+        processed_data = process_data(
             df=data,
+            has_investments=model["has_investments"],
             labels=model["labels"],
             update_info=model["update_info"],
             anchoring_info=model["anchoring"],
             purpose="simulation",
         )
+        control_data = processed_data["controls"]
+        observed_factor_data = processed_data["observed_factors"]
         data_n_obs = control_data.shape[1]
 
         if n_obs is not None and data_n_obs != n_obs:
@@ -69,7 +72,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
     else:
         control_data = jnp.ones((n_obs, 1))
         n_periods = model["dimensions"]["n_periods"]
-        observed_data = jnp.zeros((n_periods, n_obs, 0))
+        observed_factor_data = jnp.zeros((n_periods, n_obs, 0))
 
     params_index = get_params_index(
         update_info=model["update_info"],
@@ -95,7 +98,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         n_obs=n_obs,
     )
 
-    observed_data, latent_data = _simulate_dataset(
+    observed_factor_data, latent_data = _simulate_dataset(
         latent_states=states,
         covs=covs,
         log_weights=log_weights,
@@ -105,7 +108,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         n_obs=n_obs,
         update_info=model["update_info"],
         control_data=control_data,
-        observed_factor_data=observed_data,
+        observed_factor_data=observed_factor_data,
         policies=policies,
         transition_info=model["transition_info"],
     )
@@ -131,7 +134,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
                 model["labels"]["latent_factors"],
             ),
         },
-        "measurements": observed_data,
+        "measurements": observed_factor_data,
     }
 
     return out
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index c80f58c..536f39a 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -374,12 +374,13 @@ def _get_states_data(model, period, data, states, observed_factors):
         )
 
     if observed_factors:
-        _, _, _observed_arr = process_data(
+        _observed_arr = process_data(
             df=data,
+            has_investments=model["has_investments"],
             labels=model["labels"],
             update_info=model["update_info"],
             anchoring_info=model["anchoring"],
-        )
+        )["observed_factors"]
         # convert from jax to numpy
         _observed_arr = np.array(_observed_arr)
         observed_data = pd.DataFrame(

From 1feb3cd0864e63e7fb03f69a3514e97d483bbd40 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Wed, 25 Sep 2024 18:23:44 +0200
Subject: [PATCH 08/48] [WIP] Test for augmenting data with investments.

---
 src/skillmodels/process_data.py |  1 +
 tests/test_process_data.py      | 68 ++++++++++++++++++++++++++++++---
 2 files changed, 64 insertions(+), 5 deletions(-)

diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 8a210fc..882ebb9 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -15,6 +15,7 @@ def _augment_data_for_investments(data: pd.DataFrame, labels: dict[str, Any]):
 
     """
     # Make sure datset is balanced
+    breakpoint()
     assert data.reset_index()["id_kid"].nunique() * 5 == data.shape[0]
     assert set(data.reset_index()["trimester"].unique()) == set(
         TRIM_AUG_TO_TRIMESTER.values()
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index 6584aa4..499a5df 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -14,10 +14,11 @@
     _handle_controls_with_missings,
     pre_process_data,
 )
+from skillmodels.process_model import _get_labels, get_dimensions, get_has_investments
 
 
 def test_pre_process_data():
-    df = pd.DataFrame(data=np.arange(10).reshape(10, 1), columns=["var"])
+    df = pd.DataFrame(data=np.arange(20).reshape(2, 10).T, columns=["var", "inv"])
     df["period"] = [1, 2, 3, 2, 3, 4, 2, 4, 3, 1]
     df["id"] = [1, 1, 1, 3, 3, 3, 4, 4, 5, 5]
     df.set_index(["id", "period"], inplace=True)
@@ -26,14 +27,71 @@ def test_pre_process_data():
     period = [0, 1, 2, 3] * 4
     id_ = np.arange(4).repeat(4)
     nan = np.nan
-    data = [0, 1, 2, nan, nan, 3, 4, 5, nan, 6, nan, 7, 9, nan, 8, nan]
-    data = np.column_stack([period, id_, data])
-    exp = pd.DataFrame(data=data, columns=["__period__", "__id__", "var"])
+    data = {
+        "var": [0, 1, 2, nan, nan, 3, 4, 5, nan, 6, nan, 7, 9, nan, 8, nan],
+        "inv": [10, 11, 12, nan, nan, 13, 14, 15, nan, 16, nan, 17, 19, nan, 18, nan],
+    }
+    data = np.column_stack([period, id_, data["var"], data["inv"]])
+    exp = pd.DataFrame(data=data, columns=["__period__", "__id__", "var", "inv"])
     exp.set_index(["__id__", "__period__"], inplace=True)
 
     res = pre_process_data(df, [0, 1, 2, 3])
+    assert res[["var", "inv"]].equals(exp[["var", "inv"]])
 
-    assert res["var"].equals(exp["var"])
+
+def test_augment_data_for_investments():
+    df = pd.DataFrame(data=np.arange(10).reshape(2, 5).T, columns=["var", "inv"])
+    df["period"] = [1, 1, 2, 1, 2]
+    df["id"] = [1, 3, 3, 5, 5]
+    df.set_index(["id", "period"], inplace=True)
+
+    model_dict = {
+        "factors": {
+            "fac1": {
+                "measurements": ["var", "var"],
+                "transition_function": "linear",
+                "is_investment": False,
+                "is_correction": False,
+            },
+            "fac2": {
+                "measurements": ["inv", "inv"],
+                "transition_function": "linear",
+                "is_investment": True,
+                "is_correction": False,
+            },
+        },
+        "estimation_options": {},
+    }
+
+    has_investments = get_has_investments(model_dict["factors"])
+    dims = get_dimensions(model_dict, has_investments)
+    labels = _get_labels(
+        model_dict=model_dict, has_investments=has_investments, dimensions=dims
+    )
+    pre_processed = pre_process_data(df, labels["periods_raw"])
+    breakpoint()
+
+    exp = pd.DataFrame(
+        {
+            "id": {0: 0, 1: 0, 2: 1, 3: 1, 4: 2, 5: 2},
+            "period": {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 1},
+            "var": {0: 0.0, 1: nan, 2: 1.0, 3: 2.0, 4: 3.0, 5: 4.0},
+            "inv": {0: 5.0, 1: nan, 2: 6.0, 3: 7.0, 4: 8.0, 5: 9.0},
+        }
+    )
+    period = [0, 1, 2, 3] * 8
+    id_ = np.arange(4).repeat(4)
+    nan = np.nan
+    data = {
+        "var": [0, 1, 2, nan, nan, 3, 4, 5, nan, 6, nan, 7, 9, nan, 8, nan],
+        "inv": [10, 11, 12, nan, nan, 13, 14, 15, nan, 16, nan, 17, 19, nan, 18, nan],
+    }
+    data = np.column_stack([period, id_, data["var"], data["inv"]])
+    exp = pd.DataFrame(data=data, columns=["__period__", "__id__", "var", "inv"])
+    exp.set_index(["__id__", "__period__"], inplace=True)
+
+    # breakpoint()
+    assert ["var"].equals(exp["var"])
 
 
 def test_handle_controls_with_missings():

From 300376234d180f8e59a65ba4da0461ada8252d00 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Thu, 26 Sep 2024 06:01:09 +0200
Subject: [PATCH 09/48] Stack data for investments.

---
 src/skillmodels/check_model.py   |  34 ++++++--
 src/skillmodels/process_data.py  |  67 ++++++++++-----
 src/skillmodels/process_model.py |   2 +-
 tests/test_process_data.py       | 140 ++++++++++++++++++++++++-------
 4 files changed, 189 insertions(+), 54 deletions(-)

diff --git a/src/skillmodels/check_model.py b/src/skillmodels/check_model.py
index 4da8257..6d0ab20 100644
--- a/src/skillmodels/check_model.py
+++ b/src/skillmodels/check_model.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 
-def check_model(model_dict, labels, dimensions, anchoring):
+def check_model(model_dict, labels, dimensions, anchoring, has_investments):
     """Check consistency and validity of the model specification.
 
     labels, dimensions and anchoring information are done before the model checking
@@ -15,12 +15,11 @@ def check_model(model_dict, labels, dimensions, anchoring):
         model_dict (dict): The model specification. See: :ref:`model_specs`
         dimensions (dict): Dimensional information like n_states, n_periods, n_controls,
             n_mixtures. See :ref:`dimensions`.
-
         labels (dict): Dict of lists with labels for the model quantities like
             factors, periods, controls, stagemap and stages. See :ref:`labels`
-
         anchoring (dict): Dictionary with information about anchoring.
             See :ref:`anchoring`
+        has_investments (bool): Whether the model has any investment factors
 
     Raises:
         ValueError
@@ -32,7 +31,14 @@ def check_model(model_dict, labels, dimensions, anchoring):
         dimensions["n_periods"],
     )
     report += _check_anchoring(anchoring)
-    report += _check_measurements(model_dict, labels["latent_factors"])
+    invalid_measurements = _check_measurements(model_dict, labels["latent_factors"])
+    if invalid_measurements:
+        report += invalid_measurements
+    elif has_investments:
+        # Make this conditional because the check only works for valid meas.
+        report += _check_no_overlap_in_measurements_of_states_and_inv(
+            model_dict, labels
+        )
     report += _check_normalizations(model_dict, labels["latent_factors"])
 
     report = "\n".join(report)
@@ -83,7 +89,7 @@ def _check_measurements(model_dict, factors):
         candidate = model_dict["factors"][factor]["measurements"]
         if not _is_list_of(candidate, list):
             report.append(
-                f"measurements must lists of lists. Check measurements of {factor}.",
+                f"measurements must be lists of lists. Check measurements of {factor}.",
             )
         else:
             for period, meas_list in enumerate(candidate):
@@ -96,6 +102,24 @@ def _check_measurements(model_dict, factors):
     return report
 
 
+def _check_no_overlap_in_measurements_of_states_and_inv(model_dict, labels):
+    report = []
+    for period in labels["periods_raw"]:
+        meas = {}
+        for factor in labels["latent_factors"]:
+            props = model_dict["factors"][factor]
+            if props.get("is_investment", False):
+                meas["investments"] = set(props["measurements"][period])
+            else:
+                meas["states"] = set(props["measurements"][period])
+        if overlap := meas["states"].intersection(meas["investments"]):
+            report.append(
+                "Measurements for states and investments must not overlap. Check "
+                f"measurements {overlap} in period {period}.",
+            )
+    return report
+
+
 def _check_normalizations(model_dict, factors):
     report = []
     for factor in factors:
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 882ebb9..9faf68c 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -8,34 +8,61 @@
 from skillmodels.process_model import get_period_measurements
 
 
-def _augment_data_for_investments(data: pd.DataFrame, labels: dict[str, Any]):
+def _get_period_data_for_investments(
+    period: int,
+    period_raw: int,
+    df: pd.DataFrame,
+    update_info: pd.DataFrame,
+    labels: dict[str, Any],
+) -> pd.DataFrame:
+    meas = get_period_measurements(update_info, period)
+    controls = labels["controls"]
+    observed = labels["observed_factors"]
+
+    out = df.query(f"period_raw == {period_raw}")[
+        [
+            "id",
+            *meas,
+            *controls,
+            *observed,
+            "period_raw",
+            "__old_id__",
+            "__old_period__",
+        ]
+    ]
+    out["period"] = period
+    return out
+
+
+def _augment_data_for_investments(df: pd.DataFrame, model: dict[str, Any]):
     """Make room for endogenous investments by doubling up the periods.
 
     Endogeneity of investments means that current states influence the
 
     """
+    df = df.reset_index().rename(columns={"period": "period_raw"})
     # Make sure datset is balanced
-    breakpoint()
-    assert data.reset_index()["id_kid"].nunique() * 5 == data.shape[0]
-    assert set(data.reset_index()["trimester"].unique()) == set(
-        TRIM_AUG_TO_TRIMESTER.values()
+    n_ids = df["id"].nunique()
+    n_periods = df["period_raw"].nunique()
+    assert n_ids * n_periods == df.shape[0]
+    assert set(df["period_raw"]) == set(
+        model["labels"]["periods_to_periods_raw"].values()
     )
 
-    out = pd.DataFrame()
-    for trim_aug, trimester in TRIM_AUG_TO_TRIMESTER.items():
-        meas_cols = sorted(
-            {
-                m
-                for f in model_dict["factors"].values()
-                for m in f["measurements"][trim_aug]
-            }
-        )
-        cols = model_dict["observed_factors"] + meas_cols
-        period_data = data.loc[(slice(None), trimester), cols].copy()
-        period_data["trim_aug"] = trim_aug
-        out = pd.concat([out, period_data])
-
-    out = out.reset_index().set_index(["id_kid", "trim_aug"]).sort_index()
+    out = pd.concat(
+        [
+            _get_period_data_for_investments(
+                period=period,
+                period_raw=period_raw,
+                df=df,
+                update_info=model["update_info"],
+                labels=model["labels"],
+            )
+            for period, period_raw in model["labels"]["periods_to_periods_raw"].items()
+        ]
+    )
+    out = out.set_index(["id", "period"]).sort_index()
+    return out
 
 
 def process_data(
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 4af05ff..4bf4689 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -46,7 +46,7 @@ def process_model(model_dict):
         model_dict=model_dict, has_investments=has_investments, dimensions=dims
     )
     anchoring = _process_anchoring(model_dict, has_investments)
-    check_model(model_dict, labels, dims, anchoring)
+    check_model(model_dict, labels, dims, anchoring, has_investments)
     if has_investments:
         model_dict_aug = _augment_periods_for_investments(
             model_dict=model_dict,
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index 499a5df..f2f27aa 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -8,13 +8,14 @@
 from numpy.testing import assert_array_equal as aae
 
 from skillmodels.process_data import (
+    _augment_data_for_investments,
     _generate_controls_array,
     _generate_measurements_array,
     _generate_observed_factor_array,
     _handle_controls_with_missings,
     pre_process_data,
 )
-from skillmodels.process_model import _get_labels, get_dimensions, get_has_investments
+from skillmodels.process_model import process_model
 
 
 def test_pre_process_data():
@@ -40,7 +41,7 @@ def test_pre_process_data():
 
 
 def test_augment_data_for_investments():
-    df = pd.DataFrame(data=np.arange(10).reshape(2, 5).T, columns=["var", "inv"])
+    df = pd.DataFrame(data=np.arange(15).reshape(3, 5).T, columns=["var", "inv", "of"])
     df["period"] = [1, 1, 2, 1, 2]
     df["id"] = [1, 3, 3, 5, 5]
     df.set_index(["id", "period"], inplace=True)
@@ -48,50 +49,133 @@ def test_augment_data_for_investments():
     model_dict = {
         "factors": {
             "fac1": {
-                "measurements": ["var", "var"],
+                "measurements": [["var"], ["var"]],
                 "transition_function": "linear",
                 "is_investment": False,
                 "is_correction": False,
             },
             "fac2": {
-                "measurements": ["inv", "inv"],
+                "measurements": [["inv"], ["inv"]],
                 "transition_function": "linear",
                 "is_investment": True,
                 "is_correction": False,
             },
         },
+        "observed_factors": ["of"],
         "estimation_options": {},
     }
 
-    has_investments = get_has_investments(model_dict["factors"])
-    dims = get_dimensions(model_dict, has_investments)
-    labels = _get_labels(
-        model_dict=model_dict, has_investments=has_investments, dimensions=dims
-    )
-    pre_processed = pre_process_data(df, labels["periods_raw"])
-    breakpoint()
+    model = process_model(model_dict)
+    pre_processed_data = pre_process_data(df, model["labels"]["periods_raw"])
+    pre_processed_data["constant"] = 1
 
+    nan = np.nan
     exp = pd.DataFrame(
         {
-            "id": {0: 0, 1: 0, 2: 1, 3: 1, 4: 2, 5: 2},
-            "period": {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 1},
-            "var": {0: 0.0, 1: nan, 2: 1.0, 3: 2.0, 4: 3.0, 5: 4.0},
-            "inv": {0: 5.0, 1: nan, 2: 6.0, 3: 7.0, 4: 8.0, 5: 9.0},
+            "id": {
+                0: 0,
+                1: 0,
+                2: 0,
+                3: 0,
+                4: 1,
+                5: 1,
+                6: 1,
+                7: 1,
+                8: 2,
+                9: 2,
+                10: 2,
+                11: 2,
+            },
+            "period": {
+                0: 0,
+                1: 1,
+                2: 2,
+                3: 3,
+                4: 0,
+                5: 1,
+                6: 2,
+                7: 3,
+                8: 0,
+                9: 1,
+                10: 2,
+                11: 3,
+            },
+            "period_raw": {
+                0: 0,
+                1: 0,
+                2: 1,
+                3: 1,
+                4: 0,
+                5: 0,
+                6: 1,
+                7: 1,
+                8: 0,
+                9: 0,
+                10: 1,
+                11: 1,
+            },
+            "constant": {
+                0: 1,
+                1: 1,
+                2: 1,
+                3: 1,
+                4: 1,
+                5: 1,
+                6: 1,
+                7: 1,
+                8: 1,
+                9: 1,
+                10: 1,
+                11: 1,
+            },
+            "var": {
+                0: 0.0,
+                1: nan,
+                2: nan,
+                3: nan,
+                4: 1.0,
+                5: nan,
+                6: 2.0,
+                7: nan,
+                8: 3.0,
+                9: nan,
+                10: 4.0,
+                11: nan,
+            },
+            "inv": {
+                0: nan,
+                1: 5.0,
+                2: nan,
+                3: nan,
+                4: nan,
+                5: 6.0,
+                6: nan,
+                7: 7.0,
+                8: nan,
+                9: 8.0,
+                10: nan,
+                11: 9.0,
+            },
+            "of": {
+                0: 10.0,
+                1: 10.0,
+                2: nan,
+                3: nan,
+                4: 11.0,
+                5: 11.0,
+                6: 12.0,
+                7: 12.0,
+                8: 13.0,
+                9: 13.0,
+                10: 14.0,
+                11: 14.0,
+            },
         }
-    )
-    period = [0, 1, 2, 3] * 8
-    id_ = np.arange(4).repeat(4)
-    nan = np.nan
-    data = {
-        "var": [0, 1, 2, nan, nan, 3, 4, 5, nan, 6, nan, 7, 9, nan, 8, nan],
-        "inv": [10, 11, 12, nan, nan, 13, 14, 15, nan, 16, nan, 17, 19, nan, 18, nan],
-    }
-    data = np.column_stack([period, id_, data["var"], data["inv"]])
-    exp = pd.DataFrame(data=data, columns=["__period__", "__id__", "var", "inv"])
-    exp.set_index(["__id__", "__period__"], inplace=True)
+    ).set_index(["id", "period"])
 
-    # breakpoint()
-    assert ["var"].equals(exp["var"])
+    res = _augment_data_for_investments(df=pre_processed_data, model=model)
+    cols = ["period_raw", "var", "inv", "constant", "of"]
+    pd.testing.assert_frame_equal(res[cols], exp[cols])
 
 
 def test_handle_controls_with_missings():

From 96d635ebd97bb219d30cf97e8b92991f3a9b4e9a Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Thu, 26 Sep 2024 07:56:35 +0200
Subject: [PATCH 10/48] Fix interfaces.

---
 src/skillmodels/process_data.py | 118 ++++++++++++++++----------------
 tests/test_process_data.py      |   6 +-
 2 files changed, 65 insertions(+), 59 deletions(-)

diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 9faf68c..be9792c 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -8,63 +8,6 @@
 from skillmodels.process_model import get_period_measurements
 
 
-def _get_period_data_for_investments(
-    period: int,
-    period_raw: int,
-    df: pd.DataFrame,
-    update_info: pd.DataFrame,
-    labels: dict[str, Any],
-) -> pd.DataFrame:
-    meas = get_period_measurements(update_info, period)
-    controls = labels["controls"]
-    observed = labels["observed_factors"]
-
-    out = df.query(f"period_raw == {period_raw}")[
-        [
-            "id",
-            *meas,
-            *controls,
-            *observed,
-            "period_raw",
-            "__old_id__",
-            "__old_period__",
-        ]
-    ]
-    out["period"] = period
-    return out
-
-
-def _augment_data_for_investments(df: pd.DataFrame, model: dict[str, Any]):
-    """Make room for endogenous investments by doubling up the periods.
-
-    Endogeneity of investments means that current states influence the
-
-    """
-    df = df.reset_index().rename(columns={"period": "period_raw"})
-    # Make sure datset is balanced
-    n_ids = df["id"].nunique()
-    n_periods = df["period_raw"].nunique()
-    assert n_ids * n_periods == df.shape[0]
-    assert set(df["period_raw"]) == set(
-        model["labels"]["periods_to_periods_raw"].values()
-    )
-
-    out = pd.concat(
-        [
-            _get_period_data_for_investments(
-                period=period,
-                period_raw=period_raw,
-                df=df,
-                update_info=model["update_info"],
-                labels=model["labels"],
-            )
-            for period, period_raw in model["labels"]["periods_to_periods_raw"].items()
-        ]
-    )
-    out = out.set_index(["id", "period"]).sort_index()
-    return out
-
-
 def process_data(
     df, has_investments, labels, update_info, anchoring_info, purpose="estimation"
 ):
@@ -98,7 +41,7 @@ def process_data(
     out = {}
 
     if has_investments:
-        df = _augment_data_for_investments(df, labels)
+        df = _augment_data_for_investments(df, labels, update_info)
     else:
         df = _add_copies_of_anchoring_outcome(df, anchoring_info)
     _check_data(df, update_info, labels, purpose=purpose)
@@ -144,6 +87,65 @@ def pre_process_data(df, periods):
     return df
 
 
+def _get_period_data_for_investments(
+    period: int,
+    period_raw: int,
+    df: pd.DataFrame,
+    labels: dict[str, Any],
+    update_info: pd.DataFrame,
+) -> pd.DataFrame:
+    meas = get_period_measurements(update_info, period)
+    controls = labels["controls"]
+    observed = labels["observed_factors"]
+
+    out = df.query(f"period_raw == {period_raw}")[
+        [
+            "id",
+            *meas,
+            *controls,
+            *observed,
+            "period_raw",
+            "__old_id__",
+            "__old_period__",
+        ]
+    ]
+    out["period"] = period
+    return out
+
+
+def _augment_data_for_investments(
+    df: pd.DataFrame,
+    labels: dict[str, Any],
+    update_info: pd.DataFrame,
+):
+    """Make room for endogenous investments by doubling up the periods.
+
+    Endogeneity of investments means that current states influence the
+
+    """
+    df = df.reset_index().rename(columns={"period": "period_raw"})
+    # Make sure datset is balanced
+    n_ids = df["id"].nunique()
+    n_periods = df["period_raw"].nunique()
+    assert n_ids * n_periods == df.shape[0]
+    assert set(df["period_raw"]) == set(labels["periods_to_periods_raw"].values())
+
+    out = pd.concat(
+        [
+            _get_period_data_for_investments(
+                period=period,
+                period_raw=period_raw,
+                df=df,
+                update_info=update_info,
+                labels=labels,
+            )
+            for period, period_raw in labels["periods_to_periods_raw"].items()
+        ]
+    )
+    out = out.set_index(["id", "period"]).sort_index()
+    return out
+
+
 def _add_copies_of_anchoring_outcome(df, anchoring_info):
     df = df.copy()
     for factor in anchoring_info["factors"]:
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index f2f27aa..34678fb 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -173,7 +173,11 @@ def test_augment_data_for_investments():
         }
     ).set_index(["id", "period"])
 
-    res = _augment_data_for_investments(df=pre_processed_data, model=model)
+    res = _augment_data_for_investments(
+        df=pre_processed_data,
+        labels=model["labels"],
+        update_info=model["update_info"],
+    )
     cols = ["period_raw", "var", "inv", "constant", "of"]
     pd.testing.assert_frame_equal(res[cols], exp[cols])
 

From 50ce8d005028aac461d041d0a1c5a0f4370757df Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Thu, 26 Sep 2024 08:28:54 +0200
Subject: [PATCH 11/48] Readability.

---
 src/skillmodels/process_data.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index be9792c..78cbaf6 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -142,8 +142,7 @@ def _augment_data_for_investments(
             for period, period_raw in labels["periods_to_periods_raw"].items()
         ]
     )
-    out = out.set_index(["id", "period"]).sort_index()
-    return out
+    return out.set_index(["id", "period"]).sort_index()
 
 
 def _add_copies_of_anchoring_outcome(df, anchoring_info):

From 77e99615ca7b2c9e190312eeebf4d3b6e53d0c53 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Tue, 25 Feb 2025 14:13:48 +0100
Subject: [PATCH 12/48] Update environment.

---
 environment.yml |    31 -
 pixi.lock       | 18340 +++++++++++++++-------------------------------
 pyproject.toml  |    25 +-
 3 files changed, 5871 insertions(+), 12525 deletions(-)
 delete mode 100644 environment.yml

diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index 81a0ab2..0000000
--- a/environment.yml
+++ /dev/null
@@ -1,31 +0,0 @@
----
-name: skillmodels
-channels:
-  - conda-forge
-  - opensourceeconomics
-dependencies:
-  - python=3.9
-  - jax>=0.1.7
-  - jaxlib>=0.1.51
-  - pip
-  - conda-build
-  - doc8
-  - jupyter
-  - numpy
-  - pandas
-  - pytest
-  - pytest-xdist
-  - restructuredtext_lint
-  - tox-conda
-  - scipy
-  - sphinxcontrib-bibtex
-  - pydata-sphinx-theme>=0.3
-  - anaconda-client
-  - nbsphinx
-  - pdbpp
-  - filterpy
-  - dags
-  - plotly
-  - pip:
-      - pre-commit
-      - black
diff --git a/pixi.lock b/pixi.lock
index 103d95b..0fa9356 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,4 @@
-version: 5
+version: 6
 environments:
   cuda:
     channels:
@@ -9,275 +9,277 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/_sysroot_linux-64_curr_repodata_hack-3-h69a702a_16.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.4.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py312h66e93f0_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hb3c18ed_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.1.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.8.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py313h536fd9c_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.4-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.1.0-pyh71513ae_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.3-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.2.0-pyh29332c3_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.2.0-h82add2a_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py313h46c70d0_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.33.1-heb4867d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.0-py312h68727a3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.6.37-ha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.6.68-ha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.6.68-ha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.6.68-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.6.68-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.6.68-h3f2d84a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-static-12.6.68-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-64-12.6.68-h3f2d84a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-64-12.6.68-h3f2d84a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-12.6.68-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-driver-dev_linux-64-12.6.68-h3f2d84a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-12.6.68-hcdd1206_0.conda
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+  - pylint>=2.6.0 ; extra == 'dev'
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+  requires_python: '>=3.9'
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-  url: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
+  - pkg:pypi/ml-dtypes?source=hash-mapping
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+  timestamp: 1736538997988
+- conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
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-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
+  - python >=3.13,<3.14.0a0
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   license: MIT
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-  - tinycss2
-  - traitlets >=5.0
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-  name: yaml
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-  url: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
+  size: 17819
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
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   md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae
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   license: MIT
   license_family: MIT
   purls: []
   size: 89141
   timestamp: 1641346969816
-- kind: conda
-  name: yaml
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-  url: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h8ffe710_2.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h8ffe710_2.tar.bz2
   sha256: 4e2246383003acbad9682c7c63178e2e715ad0eb84f03a8df1fbfba455dfedc5
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   depends:
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+  platform: win
   license: MIT
   license_family: MIT
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   size: 63274
   timestamp: 1641347623319
-- kind: conda
-  name: zeromq
-  version: 4.3.5
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-- kind: conda
-  name: zeromq
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-  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-ha4adb4c_5.conda
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-  md5: e8372041ebb377237db9d0d24c7b5962
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda
+  sha256: a4dc72c96848f764bb5a5176aa93dd1e9b9e52804137b99daeebba277b31ea10
+  md5: 3947a35e916fcc6b9825449affbf4214
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   - libgcc >=13
   - libsodium >=1.0.20,<1.0.21.0a0
   - libstdcxx >=13
+  arch: x86_64
+  platform: linux
   license: MPL-2.0
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-  size: 353159
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-- kind: conda
-  name: zeromq
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-- kind: conda
-  name: zeromq
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   - vc >=14.2,<15
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+  arch: x86_64
+  platform: win
   license: MPL-2.0
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-  size: 2710711
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-  name: zipp
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+  size: 2527503
+  timestamp: 1731585151036
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
+  sha256: 567c04f124525c97a096b65769834b7acb047db24b15a56888a322bf3966c3e1
+  md5: 0c3cc595284c5e8f0f9900a9b228a332
   depends:
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+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
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-  size: 21110
-  timestamp: 1724731063145
-- kind: conda
-  name: zlib
-  version: 1.3.1
-  build: h2466b09_1
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-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda
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-  - ucrt >=10.0.20348.0
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-  - vc14_runtime >=14.29.30139
-  license: Zlib
-  license_family: Other
-  purls: []
-  size: 108081
-  timestamp: 1716874767420
-- kind: conda
-  name: zlib
-  version: 1.3.1
-  build: h4ab18f5_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-h4ab18f5_1.conda
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-  md5: 9653f1bf3766164d0e65fa723cabbc54
+  size: 21809
+  timestamp: 1732827613585
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py313h80202fe_1.conda
+  sha256: ea82f2b8964150a3aa7373b4697e48e64f2200fe68ae554ee85c641c692d1c97
+  md5: c178558ff516cd507763ffee230c20b2
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-  - libzlib 1.3.1 h4ab18f5_1
-  license: Zlib
-  license_family: Other
-  purls: []
-  size: 93004
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-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py312h15fbf35_1
-  build_number: 1
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
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-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  license: BSD-3-Clause
-  license_family: BSD
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-  size: 330788
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-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py312h7122b0e_1
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-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py312h7122b0e_1.conda
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-  md5: bd132ba98f3fc0a6067f355f8efe4cb6
-  depends:
-  - __osx >=10.13
+  - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
+  - libgcc >=13
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
+  arch: x86_64
+  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 410873
-  timestamp: 1725305688706
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py312h7606c53_1
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-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_1.conda
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-  md5: a92cc3435b2fd6f51463f5a4db5c50b1
+  size: 424424
+  timestamp: 1725305749031
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313h574b89f_1.conda
+  sha256: 1d2744ec0e91da267ce749e19142081472539cb140a7dad0646cd249246691fe
+  md5: 8e017aca933f4dd25491151edd3e7820
   depends:
   - cffi >=1.11
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
+  arch: x86_64
+  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls:
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-  size: 320624
-  timestamp: 1725305934189
-- kind: conda
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-  version: 0.23.0
-  build: py312hef9b889_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
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-  md5: 8b7069e9792ee4e5b4919a7a306d2e67
+  size: 325703
+  timestamp: 1725305947138
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
+  sha256: c558b9cc01d9c1444031bd1ce4b9cff86f9085765f17627a6cd85fc623c8a02b
+  md5: 4d056880988120e29d75bfff282e0f45
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  - libzlib >=1.2.13,<2.0.0a0
+  arch: x86_64
+  platform: linux
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 554846
+  timestamp: 1714722996770
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_1.conda
+  sha256: 532d3623961e34c53aba98db2ad0a33b7a52ff90d6960e505fb2d2efc06bb7da
+  md5: 02e4e2fa41a6528afba2e54cbc4280ff
   depends:
   - __glibc >=2.17,<3.0.a0
-  - cffi >=1.11
   - libgcc >=13
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  arch: x86_64
+  platform: linux
   license: BSD-3-Clause
   license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 419552
-  timestamp: 1725305670210
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: h0ea2cb4_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
+  purls: []
+  size: 567419
+  timestamp: 1740255350233
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
   sha256: 768e30dc513568491818fb068ee867c57c514b553915536da09e5d10b4ebf3c3
   md5: 9a17230f95733c04dc40a2b1e5491d74
   depends:
@@ -15943,57 +9353,25 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
+  arch: x86_64
+  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
   size: 349143
   timestamp: 1714723445995
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: h915ae27_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.6-h915ae27_0.conda
-  sha256: efa04a98cb149643fa54c4dad5a0179e36a5fbc88427ea0eec88ceed87fd0f96
-  md5: 4cb2cd56f039b129bb0e491c1164167e
-  depends:
-  - __osx >=10.9
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 498900
-  timestamp: 1714723303098
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: ha6fb4c9_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
-  sha256: c558b9cc01d9c1444031bd1ce4b9cff86f9085765f17627a6cd85fc623c8a02b
-  md5: 4d056880988120e29d75bfff282e0f45
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_1.conda
+  sha256: a59b096b95f20910158c927797e9144ed9c7970f1b4aca58e6d6c8db9f653006
+  md5: bf190adcc22f146d8ec66da215c9d78b
   depends:
-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 554846
-  timestamp: 1714722996770
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: hb46c0d2_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
-  sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09
-  md5: d96942c06c3e84bfcc5efb038724a7fd
-  depends:
-  - __osx >=11.0
-  - libzlib >=1.2.13,<2.0.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  arch: x86_64
+  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 405089
-  timestamp: 1714723101397
+  size: 353182
+  timestamp: 1740255407949
diff --git a/pyproject.toml b/pyproject.toml
index 0713fd6..346509d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -84,7 +84,7 @@ allow-direct-references = true
 
 [tool.pixi.project]
 channels = ["conda-forge"]
-platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"]
+platforms = ["linux-64", "win-64"]
 
 # Development Dependencies (conda)
 # --------------------------------------------------------------------------------------
@@ -92,23 +92,23 @@ platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"]
 [tool.pixi.dependencies]
 filterpy = "*"
 jupyterlab = "*"
-nbformat = ">=5.10.4,<6"
+nbformat = "*"
+networkx = "*"
 optimagic = "*"
-pixi-kernel = ">=0.4.0,<0.5"
+pixi-kernel = "*"
 plotly = ">=5.2,<6"
 pre-commit = "*"
 pybaum = "*"
-python = ">=3.12.5"
+python = "~=3.13"
 python-kaleido = "*"
-scipy = "<=1.13"
+scipy = "*"
 
 
 # Development Dependencies (pypi)
 # --------------------------------------------------------------------------------------
 
 [tool.pixi.target.unix.dependencies]
-jax = ">=0.4.20"
-jaxlib = ">=0.4.20"
+jaxlib = ">=0.5"
 
 # Development Dependencies (pypi)
 # --------------------------------------------------------------------------------------
@@ -118,20 +118,19 @@ pdbp = "*"
 skillmodels = {path = ".", editable = true}
 
 [tool.pixi.target.win-64.pypi-dependencies]
-jax = { version = ">=0.4.20", extras = ["cpu"] }
-jaxlib = ">=0.4.20"
+jax = { version = ">=0.5", extras = ["cpu"] }
+jaxlib = ">=0.5"
 
 # Features and Tasks
 # --------------------------------------------------------------------------------------
 
 [tool.pixi.feature.cuda]
-platforms = ["linux-64"]
+platforms = ["linux-64", "osx-64", "osx-arm64"]
 system-requirements = {cuda = "12"}
 
-[tool.pixi.feature.cuda.target.linux-64.dependencies]
+[tool.pixi.feature.cuda.target.unix.dependencies]
 cuda-nvcc = ">=12"
-jax = ">=0.4.20"
-jaxlib = { version = ">=0.4.20", build = "cuda12*" }
+jaxlib = { version = ">=0.5", build = "cuda12*" }
 
 [tool.pixi.feature.test.dependencies]
 pytest = "*"

From deb769d3e0dc270ab56eb5003b045c2af8a5033e Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Tue, 25 Feb 2025 18:59:33 +0100
Subject: [PATCH 13/48] Jax 0.5 installed via pypi, only way that works right
 now.

---
 pixi.lock      | 661 +++----------------------------------------------
 pyproject.toml |   8 +-
 2 files changed, 47 insertions(+), 622 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 0fa9356..1eb2346 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -277,7 +277,9 @@ environments:
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-  arch: x86_64
-  platform: linux
   license: GPL-2.0-or-later
   license_family: GPL
   purls: []
@@ -3143,8 +3065,6 @@ packages:
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   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls:
@@ -3160,8 +3080,6 @@ packages:
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   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls:
@@ -3208,8 +3126,6 @@ packages:
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   - libgcc >=13
   - libstdcxx >=13
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -3222,8 +3138,6 @@ packages:
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   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -3244,8 +3158,6 @@ packages:
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   - libzlib >=1.3.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: x86_64
-  platform: linux
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
@@ -3290,8 +3202,6 @@ packages:
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   - libexpat 2.6.4 h5888daf_0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -3363,8 +3273,6 @@ packages:
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   - libuuid >=2.38.1,<3.0a0
   - libzlib >=1.3.1,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -3381,8 +3289,6 @@ packages:
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   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -3421,8 +3327,6 @@ packages:
   - munkres
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   - python_abi 3.13.* *_cp313
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls:
@@ -3440,8 +3344,6 @@ packages:
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   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls:
@@ -3467,8 +3369,6 @@ packages:
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   - libpng >=1.6.39,<1.7.0a0
   - libzlib >=1.2.13,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: GPL-2.0-only OR FTL
   purls: []
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@@ -3482,8 +3382,6 @@ packages:
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   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: GPL-2.0-only OR FTL
   purls: []
   size: 510306
@@ -3499,8 +3397,6 @@ packages:
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   - libstdcxx >=14.2.0
   - sysroot_linux-64
-  arch: x86_64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -3513,8 +3409,6 @@ packages:
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   - gcc_impl_linux-64 14.2.0.*
   - sysroot_linux-64
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -3532,8 +3426,6 @@ packages:
   - libgettextpo 0.23.1 h5888daf_0
   - libgettextpo-devel 0.23.1 h5888daf_0
   - libstdcxx >=13
-  arch: x86_64
-  platform: linux
   license: LGPL-2.1-or-later AND GPL-3.0-or-later
   purls: []
   size: 484344
@@ -3544,8 +3436,6 @@ packages:
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   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: GPL-3.0-or-later
   license_family: GPL
   purls: []
@@ -3562,8 +3452,6 @@ packages:
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   - nettle >=3.9.1,<3.10.0a0
   - p11-kit >=0.24.1,<0.25.0a0
-  arch: x86_64
-  platform: linux
   license: LGPL-2.1-or-later
   license_family: LGPL
   purls: []
@@ -3575,8 +3463,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LGPL-2.0-or-later
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@@ -3589,8 +3475,6 @@ packages:
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   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: LGPL-2.0-or-later
   license_family: LGPL
   purls: []
@@ -3605,8 +3489,6 @@ packages:
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   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls:
@@ -3622,8 +3504,6 @@ packages:
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-  arch: x86_64
-  platform: win
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   license_family: MIT
   purls:
@@ -3638,8 +3518,6 @@ packages:
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   - sysroot_linux-64
   - tzdata
-  arch: x86_64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -3653,8 +3531,6 @@ packages:
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   - gxx_impl_linux-64 14.2.0.*
   - sysroot_linux-64
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -3699,8 +3575,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -3720,8 +3594,6 @@ packages:
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-  arch: x86_64
-  platform: win
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   purls: []
@@ -3787,8 +3659,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
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@@ -3801,8 +3671,6 @@ packages:
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-  arch: x86_64
-  platform: win
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   license_family: MIT
   purls: []
@@ -3871,8 +3739,6 @@ packages:
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   sha256: 0fd2b0b84c854029041b0ede8f4c2369242ee92acc0092f8407b1fe9238a8209
   md5: 2d89243bfb53652c182a7c73182cce4f
-  arch: x86_64
-  platform: win
   license: LicenseRef-IntelSimplifiedSoftwareOct2022
   license_family: Proprietary
   purls: []
@@ -3986,6 +3852,35 @@ packages:
   - pkg:pypi/isoduration?source=hash-mapping
   size: 19832
   timestamp: 1733493720346
+- pypi: https://files.pythonhosted.org/packages/f4/58/cc0721a1030fcbab0984beea0bf3c4610ec103f738423cdfa9c4ceb40598/jax-0.5.0-py3-none-any.whl
+  name: jax
+  version: 0.5.0
+  sha256: b3907aa87ae2c340b39cdbf80c07a74550369cafcaf7398fb60ba58d167345ab
+  requires_dist:
+  - jaxlib<=0.5.0,>=0.5.0
+  - ml-dtypes>=0.4.0
+  - numpy>=1.25
+  - numpy>=1.26.0 ; python_full_version >= '3.12'
+  - opt-einsum
+  - scipy>=1.11.1
+  - jaxlib==0.5.0 ; extra == 'minimum-jaxlib'
+  - jaxlib==0.4.38 ; extra == 'ci'
+  - jaxlib<=0.5.0,>=0.5.0 ; extra == 'tpu'
+  - libtpu-nightly==0.1.dev20241010+nightly.cleanup ; extra == 'tpu'
+  - libtpu==0.0.8 ; extra == 'tpu'
+  - requests ; extra == 'tpu'
+  - jaxlib==0.5.0 ; extra == 'cuda'
+  - jax-cuda12-plugin[with-cuda]<=0.5.0,>=0.5.0 ; extra == 'cuda'
+  - jaxlib==0.5.0 ; extra == 'cuda12'
+  - jax-cuda12-plugin[with-cuda]<=0.5.0,>=0.5.0 ; extra == 'cuda12'
+  - jaxlib==0.5.0 ; extra == 'cuda12-pip'
+  - jax-cuda12-plugin[with-cuda]<=0.5.0,>=0.5.0 ; extra == 'cuda12-pip'
+  - jaxlib==0.5.0 ; extra == 'cuda12-local'
+  - jax-cuda12-plugin==0.5.0 ; extra == 'cuda12-local'
+  - jaxlib==0.5.0 ; extra == 'rocm'
+  - jax-rocm60-plugin<=0.5.0,>=0.5.0 ; extra == 'rocm'
+  - kubernetes ; extra == 'k8s'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/07/98/a507abd78e4a5cacfe8fbf92061853149e6abeaf1b3028fe353ca04cbbee/jax-0.5.1-py3-none-any.whl
   name: jax
   version: 0.5.1
@@ -4042,8 +3937,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: Apache-2.0
   purls:
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@@ -4087,8 +3980,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: Apache-2.0
   purls:
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-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
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   - python_abi 3.13.* *_cp313
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
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-  platform: linux
   license: MIT
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-  arch: x86_64
-  platform: win
   license: MIT
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@@ -4463,8 +4344,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
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-  arch: x86_64
-  platform: win
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-  platform: linux
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-  arch: x86_64
-  platform: win
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   license: Apache-2.0
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-  arch: x86_64
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@@ -4649,8 +4506,6 @@ packages:
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@@ -4668,8 +4523,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -4686,8 +4539,6 @@ packages:
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   - liblapack =3.9.0=31*_mkl
-  arch: x86_64
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   license: BSD-3-Clause
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@@ -4699,8 +4550,6 @@ packages:
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@@ -4713,8 +4562,6 @@ packages:
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-  arch: x86_64
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@@ -4727,8 +4574,6 @@ packages:
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-  platform: linux
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@@ -4742,8 +4587,6 @@ packages:
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-  arch: x86_64
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@@ -4756,8 +4599,6 @@ packages:
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-  arch: x86_64
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@@ -4820,8 +4655,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-NVIDIA-End-User-License-Agreement
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@@ -4886,8 +4713,6 @@ packages:
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@@ -4900,8 +4725,6 @@ packages:
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@@ -4917,8 +4740,6 @@ packages:
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-  arch: x86_64
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   license: LicenseRef-NVIDIA-End-User-License-Agreement
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@@ -4931,8 +4752,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -4946,8 +4765,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-NVIDIA-End-User-License-Agreement
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@@ -4963,8 +4780,6 @@ packages:
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-  arch: x86_64
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@@ -4981,8 +4796,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: curl
   license_family: MIT
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@@ -4999,8 +4812,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-NVIDIA-End-User-License-Agreement
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@@ -5016,8 +4827,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -5031,8 +4840,6 @@ packages:
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-  platform: linux
   license: LicenseRef-NVIDIA-End-User-License-Agreement
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@@ -5049,8 +4856,6 @@ packages:
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-  platform: linux
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@@ -5075,8 +4878,6 @@ packages:
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-  arch: x86_64
-  platform: win
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@@ -5089,8 +4890,6 @@ packages:
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-  platform: linux
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   license_family: MIT
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@@ -5104,8 +4903,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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-  arch: x86_64
-  platform: linux
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-  platform: linux
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   license_family: MIT
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-  platform: win
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-  arch: x86_64
-  platform: linux
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   license_family: GPL
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-  arch: x86_64
-  platform: win
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-  arch: x86_64
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-  arch: x86_64
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@@ -5311,8 +5082,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-libglvnd
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@@ -5329,8 +5098,6 @@ packages:
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-  arch: x86_64
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@@ -5349,8 +5116,6 @@ packages:
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-  platform: win
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@@ -5360,8 +5125,6 @@ packages:
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-  platform: linux
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@@ -5373,8 +5136,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-libglvnd
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@@ -5384,8 +5145,6 @@ packages:
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-  arch: x86_64
-  platform: win
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   license_family: GPL
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@@ -5422,8 +5179,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: Apache-2.0
   license_family: APACHE
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@@ -5438,8 +5193,6 @@ packages:
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-  arch: x86_64
-  platform: win
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-  platform: linux
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@@ -5464,8 +5215,6 @@ packages:
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-  arch: x86_64
-  platform: win
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@@ -5477,8 +5226,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -5488,8 +5235,6 @@ packages:
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-  platform: win
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@@ -5501,8 +5246,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: IJG AND BSD-3-Clause AND Zlib
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@@ -5516,8 +5259,6 @@ packages:
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-  arch: x86_64
-  platform: win
   license: IJG AND BSD-3-Clause AND Zlib
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@@ -5532,8 +5273,6 @@ packages:
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   - liblapacke =3.9.0=31*_openblas
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-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
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@@ -5549,8 +5288,6 @@ packages:
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   - liblapacke =3.9.0=31*_mkl
-  arch: x86_64
-  platform: win
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   license_family: BSD
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-  arch: x86_64
-  platform: linux
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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-  arch: x86_64
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@@ -5605,8 +5336,6 @@ packages:
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-  platform: linux
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-  arch: x86_64
-  platform: win
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-  arch: x86_64
-  platform: linux
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@@ -5678,8 +5399,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-NVIDIA-End-User-License-Agreement
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-  arch: x86_64
-  platform: linux
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-  arch: x86_64
-  platform: linux
   license: LicenseRef-libglvnd
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-  arch: x86_64
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-  arch: x86_64
-  platform: win
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@@ -5762,8 +5471,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: PostgreSQL
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-  platform: linux
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-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
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@@ -5810,8 +5513,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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-  arch: x86_64
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@@ -5848,8 +5545,6 @@ packages:
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-  arch: x86_64
-  platform: win
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@@ -5875,8 +5568,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -5963,8 +5646,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -6029,8 +5702,6 @@ packages:
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-  arch: x86_64
-  platform: win
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-  arch: x86_64
-  platform: win
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-  arch: x86_64
-  platform: linux
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   license_family: MIT
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-  arch: x86_64
-  platform: win
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-  platform: linux
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-  platform: win
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-  platform: linux
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-  arch: x86_64
-  platform: win
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   license_family: MIT
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@@ -6170,8 +5825,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: Zlib
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   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls:
@@ -8737,8 +8234,6 @@ packages:
   md5: 00cf3a61562bd53bd5ea99e6888793d0
   depends:
   - vc14_runtime >=14.40.33810
-  arch: x86_64
-  platform: win
   track_features:
   - vc14
   license: BSD-3-Clause
@@ -8753,8 +8248,6 @@ packages:
   - ucrt >=10.0.20348.0
   constrains:
   - vs2015_runtime 14.42.34433.* *_24
-  arch: x86_64
-  platform: win
   license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
   license_family: Proprietary
   purls: []
@@ -8779,8 +8272,6 @@ packages:
   md5: 117fcc5b86c48f3b322b0722258c7259
   depends:
   - vc14_runtime >=14.42.34433
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8795,8 +8286,6 @@ packages:
   - libffi >=3.4,<4.0a0
   - libgcc-ng >=13
   - libstdcxx-ng >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8870,8 +8359,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8887,8 +8374,6 @@ packages:
   - libxcb >=1.16,<2.0.0a0
   - xcb-util-image >=0.4.0,<0.5.0a0
   - xcb-util-renderutil >=0.3.10,<0.4.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8901,8 +8386,6 @@ packages:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
   - xcb-util >=0.4.1,<0.5.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8914,8 +8397,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8927,8 +8408,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8940,8 +8419,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8954,8 +8431,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8967,8 +8442,6 @@ packages:
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8982,8 +8455,6 @@ packages:
   - libgcc >=13
   - libuuid >=2.38.1,<3.0a0
   - xorg-libice >=1.1.2,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -8996,8 +8467,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - libxcb >=1.17.0,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9009,8 +8478,6 @@ packages:
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9023,8 +8490,6 @@ packages:
   - libgcc >=13
   - libwinpthread >=12.0.0.r4.gg4f2fc60ca
   - ucrt >=10.0.20348.0
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -9038,8 +8503,6 @@ packages:
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9054,8 +8517,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
   - xorg-libxrender >=0.9.11,<0.10.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9070,8 +8531,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9083,8 +8542,6 @@ packages:
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9097,8 +8554,6 @@ packages:
   - libgcc >=13
   - libwinpthread >=12.0.0.r4.gg4f2fc60ca
   - ucrt >=10.0.20348.0
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -9111,8 +8566,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9125,8 +8578,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9141,8 +8592,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9157,8 +8606,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxrender >=0.9.11,<0.10.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9171,8 +8618,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9187,8 +8632,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxi >=1.7.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9202,8 +8645,6 @@ packages:
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9214,8 +8655,6 @@ packages:
   md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae
   depends:
   - libgcc-ng >=9.4.0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -9227,8 +8666,6 @@ packages:
   depends:
   - vc >=14.1,<15.0a0
   - vs2015_runtime >=14.16.27012
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -9243,8 +8680,6 @@ packages:
   - libgcc >=13
   - libsodium >=1.0.20,<1.0.21.0a0
   - libstdcxx >=13
-  arch: x86_64
-  platform: linux
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -9259,8 +8694,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -9288,8 +8721,6 @@ packages:
   - python_abi 3.13.* *_cp313
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -9308,8 +8739,6 @@ packages:
   - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -9323,8 +8752,6 @@ packages:
   - libgcc-ng >=12
   - libstdcxx-ng >=12
   - libzlib >=1.2.13,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -9338,8 +8765,6 @@ packages:
   - libgcc >=13
   - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -9353,8 +8778,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -9368,8 +8791,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
diff --git a/pyproject.toml b/pyproject.toml
index 346509d..6eb5562 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -107,7 +107,11 @@ scipy = "*"
 # Development Dependencies (pypi)
 # --------------------------------------------------------------------------------------
 
-[tool.pixi.target.unix.dependencies]
+# [tool.pixi.target.unix.dependencies]
+# jaxlib = ">=0.5"
+
+[tool.pixi.target.unix.pypi-dependencies]
+jax = ">=0.5"
 jaxlib = ">=0.5"
 
 # Development Dependencies (pypi)
@@ -130,7 +134,7 @@ system-requirements = {cuda = "12"}
 
 [tool.pixi.feature.cuda.target.unix.dependencies]
 cuda-nvcc = ">=12"
-jaxlib = { version = ">=0.5", build = "cuda12*" }
+# jaxlib = { version = ">=0.5", build = "cuda12*" }
 
 [tool.pixi.feature.test.dependencies]
 pytest = "*"

From e3e148b2f618db053cfc02aaadac6afe25cb8bfd Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Tue, 25 Feb 2025 19:00:32 +0100
Subject: [PATCH 14/48] [WIP] implement fixed constraints right away.

---
 src/skillmodels/constraints.py         |  7 ++-
 src/skillmodels/maximization_inputs.py | 62 ++++++++++++++++++++++++--
 tests/test_constraints.py              |  1 +
 3 files changed, 65 insertions(+), 5 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index fa71942..4028f30 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -7,7 +7,9 @@
 import skillmodels.transition_functions as tf
 
 
-def get_constraints(dimensions, labels, anchoring_info, update_info, normalizations):
+def get_constraint_tuples(
+    dimensions, labels, anchoring_info, update_info, normalizations
+):
     """Generate constraints implied by the model specification.
 
     The result can easily be converted to optimagic-style constraints.
@@ -71,6 +73,9 @@ def add_bounds(params_df, bounds_distance=0.0):
         )
         if "lower_bound" not in df.columns:
             df["lower_bound"] = -np.inf
+        if "upper_bound" not in df.columns:
+            df["upper_bound"] = np.inf
+
         df.loc["meas_sds", "lower_bound"] = bounds_distance
         df.loc["shock_sds", "lower_bound"] = bounds_distance
 
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 21ac67d..1e446b5 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -1,13 +1,15 @@
 import functools
+from typing import Any
 
 import jax
 import jax.numpy as jnp
 import numpy as np
+import optimagic as om
 import pandas as pd
 
 import skillmodels.likelihood_function as lf
 import skillmodels.likelihood_function_debug as lfd
-from skillmodels.constraints import add_bounds, get_constraints
+from skillmodels.constraints import add_bounds, get_constraint_tuples
 from skillmodels.kalman_filters import calculate_sigma_scaling_factor_and_weights
 from skillmodels.params_index import get_params_index
 from skillmodels.parse_params import create_parsing_info
@@ -41,7 +43,7 @@ def get_maximization_inputs(model_dict, data):
         constraints (list): List of optimagic constraints that are implied by the
             model specification.
         params_template (pd.DataFrame): Parameter DataFrame with correct index and
-            bounds but with empty value column.
+            bounds. The value column is empty except for the fixed constraints.
         data_aug (pd.DataFrame): DataFrame with augmented data. If model contains
             investment factors, we double up the number of periods in order to add
 
@@ -123,7 +125,7 @@ def debug_loglike(params):
         tmp["value"] = float(tmp["value"])
         return process_debug_data(debug_data=tmp, model=model)
 
-    constr = get_constraints(
+    _constraints_tuples = get_constraint_tuples(
         dimensions=model["dimensions"],
         labels=model["labels"],
         anchoring_info=model["anchoring"],
@@ -131,18 +133,24 @@ def debug_loglike(params):
         normalizations=model["normalizations"],
     )
 
+    constraints = convert_old_style_constraints(_constraints_tuples)
+
     params_template = pd.DataFrame(columns=["value"], index=p_index)
     params_template = add_bounds(
         params_template,
         model["estimation_options"]["bounds_distance"],
     )
+    params_template = _fill_fixed_constraints(
+        params_template=params_template,
+        constraints_tuples=_constraints_tuples,
+    )
 
     out = {
         "loglike": loglike,
         "loglikeobs": loglikeobs,
         "debug_loglike": debug_loglike,
         "loglike_and_gradient": loglike_and_gradient,
-        "constraints": constr,
+        "constraints": constraints,
         "params_template": params_template,
     }
 
@@ -218,3 +226,49 @@ def _get_jnp_params_vec(params, target_index):
 
     vec = jnp.array(params.reindex(target_index)["value"].to_numpy())
     return vec
+
+
+def _sel(params, loc):
+    return params.loc[loc]
+
+
+def convert_old_style_constraints(old_style):
+    # Need this in many cases, anyhow -- so just impose to simplify code below!
+    new_style = []
+    for oc in old_style:
+        if oc["type"] == "pairwise_equality":
+            new_style.append(
+                om.PairwiseEqualityConstraint(
+                    selectors=[functools.partial(_sel, loc=loc) for loc in oc["locs"]]
+                )
+            )
+        else:
+            sel = functools.partial(_sel, loc=oc["loc"])
+            if oc["type"] == "fixed":
+                new_style.append(om.FixedConstraint(selector=sel))
+            elif oc["type"] == "equality":
+                new_style.append(om.EqualityConstraint(selector=sel))
+            elif oc["type"] == "probability":
+                new_style.append(om.ProbabilityConstraint(selector=sel))
+            elif oc["type"] == "increasing":
+                new_style.append(om.IncreasingConstraint(selector=sel))
+            else:
+                raise TypeError(oc["type"])
+    return new_style
+
+
+def _fill_fixed_constraints(
+    params_template: pd.DataFrame,
+    constraints_tuples: list[dict[str, Any]],
+) -> pd.DataFrame:
+    params = params_template.copy()
+    for constr in constraints_tuples:
+        if constr["type"] == "fixed":
+            params.loc[constr["loc"], "value"] = constr["value"]
+
+    # Check that fixed constraints are valid
+    fixed = params[params["value"].notna()]
+    invalid = fixed.query("value < lower_bound or value > upper_bound")
+    if len(invalid) > 0:
+        raise ValueError(f"Invalid fixed constraints:\n\n{invalid}")
+    return params
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index c31c89e..e647e0c 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -28,6 +28,7 @@ def test_add_bounds():
     )
     expected = df.copy(deep=True)
     expected["lower_bound"] = [0.1] * 5 + [0.1, -np.inf, 0.1, -np.inf, 0.1]
+    expected["upper_bound"] = np.inf
 
     calculated = add_bounds(df, 0.1)
     assert_frame_equal(calculated, expected)

From 75b9b8199ab396001962d3cbc65e3581e803a5a8 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Wed, 26 Feb 2025 08:56:59 +0100
Subject: [PATCH 15/48] Refactor, enforce bounds on fixed parameters.

---
 src/skillmodels/constraints.py         | 257 +++++++++++++++++--------
 src/skillmodels/maximization_inputs.py |  70 ++-----
 tests/test_constraints.py              |  38 ++--
 3 files changed, 214 insertions(+), 151 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 4028f30..f939022 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -1,15 +1,19 @@
 """List of constraints for a model, which can be converted to optimagic constraints."""
 
+import functools
 import warnings
+from typing import Any
 
 import numpy as np
+import optimagic as om
+import pandas as pd
 
 import skillmodels.transition_functions as tf
 
 
-def get_constraint_tuples(
+def get_constraints_dicts(
     dimensions, labels, anchoring_info, update_info, normalizations
-):
+) -> list[dict]:
     """Generate constraints implied by the model specification.
 
     The result can easily be converted to optimagic-style constraints.
@@ -27,45 +31,55 @@ def get_constraint_tuples(
             loadings and intercepts for each factor. See :ref:`normalizations`.
 
     Returns:
-        list[dict]: List of constraints.
+        A list of constraints with entries:
+        - "type": str, one of "fixed", "equality", "probability", "increasing",
+            "pairwise_equality". Must map to an optimagic constraint, see
+            :func:`constraints_dicts_to_om`.
+        - "loc": The location of the affected row(s) in the params DataFrame
+        - "value": float, only present if type is "fixed"
+        - "description": str, optional description of the constraint
 
     """
-    constr = []
+    c_d = []
 
-    constr += _get_normalization_constraints(normalizations, labels["latent_factors"])
-    constr += _get_mixture_weights_constraints(dimensions["n_mixtures"])
-    constr += _get_stage_constraints(labels["stagemap"], labels["stages"])
-    constr += _get_constant_factors_constraints(labels)
-    constr += _get_initial_states_constraints(
+    c_d += _get_normalization_constraints(normalizations, labels["latent_factors"])
+    c_d += _get_mixture_weights_constraints(dimensions["n_mixtures"])
+    c_d += _get_stage_constraints(labels["stagemap"], labels["stages"])
+    c_d += _get_constant_factors_constraints(labels)
+    c_d += _get_initial_states_constraints(
         dimensions["n_mixtures"],
         labels["latent_factors"],
     )
-    constr += _get_transition_constraints(labels)
-    constr += _get_anchoring_constraints(
+    c_d += _get_transition_constraints(labels)
+    c_d += _get_anchoring_constraints(
         update_info,
         labels["controls"],
         anchoring_info,
         labels["periods"],
     )
 
-    for i, c in enumerate(constr):
+    for i, c in enumerate(c_d):
         c["id"] = i
 
-    return constr
+    return c_d
 
 
-def add_bounds(params_df, bounds_distance=0.0):
-    """Add the bounds to params_df that are not implied by other constraints.
+def add_bounds(params: pd.DataFrame, bounds_distance: float) -> pd.DataFrame:
+    """Add bounds for standard deviations to params.
+
+    Lower and upper bounds are set to (minus) infinity; lower bounds for standard
+    deviation-like parameters are set to *bounds_distance*. Note that the latter will be
+    overridden for parameters where fixed constraints are imposed.
 
     Args:
-        params_df (DataFrame): see :ref:`params_df`.
-        bounds_distance (float): sets bounds stricter by this amount. Default 0.0.
+        params: see :ref:`params`.
+        bounds_distance: set standard deviation-like to this amount.
 
     Returns:
-        df (DataFrame): modified copy of params_df
+        Modified copy of params
 
     """
-    df = params_df.copy()
+    df = params.copy()
     with warnings.catch_warnings():
         warnings.filterwarnings(
             "ignore",
@@ -100,7 +114,7 @@ def _is_diagonal_entry(ind_tup):
     return is_diag
 
 
-def _get_normalization_constraints(normalizations, factors):
+def _get_normalization_constraints(normalizations, factors) -> list[dict]:
     """List of constraints to enforce normalizations.
 
     Args:
@@ -108,49 +122,44 @@ def _get_normalization_constraints(normalizations, factors):
         loadings and intercepts for each factor. See :ref:`normalizations`.
 
     Returns:
-        constraints (list)
+        constraints_dicts
 
     """
     msg = "This constraint was generated because of an explicit normalization."
     periods = range(len(normalizations[factors[0]]["loadings"]))
 
-    index_tuples = []
-    fixed_values = []
-
+    constraints_dicts = []
     for factor in factors:
         if "variances" in normalizations[factor]:
             raise ValueError("normalization for variances cannot be provided")
         for period in periods:
-            loading_norminfo = normalizations[factor]["loadings"][period]
-            for meas, normval in loading_norminfo.items():
-                index_tuples.append(("loadings", period, meas, factor))
-                fixed_values.append(normval)
-
-            intercept_norminfo = normalizations[factor]["intercepts"][period]
-            for meas, normval in intercept_norminfo.items():
-                index_tuples.append(("controls", period, meas, "constant"))
-                fixed_values.append(normval)
-
-    if index_tuples:
-        constraints = [
-            {
-                "loc": index_tuples,
-                "type": "fixed",
-                "value": fixed_values,
-                "description": msg,
-            },
-        ]
-    else:
-        constraints = []
+            for meas, normval in normalizations[factor]["loadings"][period].items():
+                constraints_dicts.append(
+                    {
+                        "loc": ("loadings", period, meas, factor),
+                        "type": "fixed",
+                        "value": normval,
+                        "description": msg,
+                    }
+                )
+            for meas, normval in normalizations[factor]["intercepts"][period].items():
+                constraints_dicts.append(
+                    {
+                        "loc": ("controls", period, meas, "constant"),
+                        "type": "fixed",
+                        "value": normval,
+                        "description": msg,
+                    }
+                )
 
-    return constraints
+    return constraints_dicts
 
 
-def _get_mixture_weights_constraints(n_mixtures):
+def _get_mixture_weights_constraints(n_mixtures) -> list[dict]:
     """Constrain mixture weights to be between 0 and 1 and sum to 1."""
     if n_mixtures == 1:
         msg = "Set the mixture weight to 1 if there is only one mixture element."
-        return [
+        constraints_dicts = [
             {
                 "loc": "mixture_weights",
                 "type": "fixed",
@@ -158,24 +167,28 @@ def _get_mixture_weights_constraints(n_mixtures):
                 "description": msg,
             },
         ]
-    msg = "Ensure that weights are between 0 and 1 and sum to 1."
-    return [{"loc": "mixture_weights", "type": "probability", "description": msg}]
+    else:
+        msg = "Ensure that weights are between 0 and 1 and sum to 1."
+        constraints_dicts = [
+            {"loc": "mixture_weights", "type": "probability", "description": msg}
+        ]
+    return constraints_dicts
 
 
-def _get_stage_constraints(stagemap, stages):
+def _get_stage_constraints(stagemap, stages) -> list[dict]:
     """Equality constraints for transition and shock parameters within stages.
 
     Args:
         stagemap (list): map periods to stages
         stages (list): stages
     Returns:
-        constrainst (list)
+        constraints_dicts
 
     """
     msg = (
         "This constraint was generated because all involved periods belong to stage {}."
     )
-    constraints = []
+    constraints_dicts = []
 
     stages_to_periods = {stage: [] for stage in stages}
     for period, stage in enumerate(stagemap):
@@ -185,14 +198,14 @@ def _get_stage_constraints(stagemap, stages):
         if len(stage_periods) > 1:
             locs_trans = [("transition", p) for p in stage_periods]
             locs_q = [("shock_sds", p) for p in stage_periods]
-            constraints.append(
+            constraints_dicts.append(
                 {
                     "locs": locs_trans,
                     "type": "pairwise_equality",
                     "description": msg.format(stage),
                 },
             )
-            constraints.append(
+            constraints_dicts.append(
                 {
                     "locs": locs_q,
                     "type": "pairwise_equality",
@@ -200,10 +213,10 @@ def _get_stage_constraints(stagemap, stages):
                 },
             )
 
-    return constraints
+    return constraints_dicts
 
 
-def _get_constant_factors_constraints(labels):
+def _get_constant_factors_constraints(labels) -> list[dict]:
     """Fix shock variances of constant factors to 0.
 
     Args:
@@ -211,15 +224,15 @@ def _get_constant_factors_constraints(labels):
             factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     Returns:
-        constraints (list)
+        constraints_dicts
 
     """
-    constraints = []
+    constraints_dicts = []
     for f, factor in enumerate(labels["latent_factors"]):
         if labels["transition_names"][f] == "constant":
             msg = f"This constraint was generated because {factor} is constant."
             for period in labels["periods"][:-1]:
-                constraints.append(
+                constraints_dicts.append(
                     {
                         "loc": ("shock_sds", period, factor, "-"),
                         "type": "fixed",
@@ -227,10 +240,10 @@ def _get_constant_factors_constraints(labels):
                         "description": msg,
                     },
                 )
-    return constraints
+    return constraints_dicts
 
 
-def _get_initial_states_constraints(n_mixtures, factors):
+def _get_initial_states_constraints(n_mixtures, factors) -> list[dict]:
     """Enforce that the x values of the first factor are increasing.
 
     Otherwise the model would only be identified up to the order of the start factors.
@@ -240,7 +253,7 @@ def _get_initial_states_constraints(n_mixtures, factors):
         factors (list): the latent factors of the model
 
     Returns:
-        constraints (list)
+        constraints_dicts
 
     """
     msg = (
@@ -250,18 +263,18 @@ def _get_initial_states_constraints(n_mixtures, factors):
     )
 
     if n_mixtures > 1:
-        ind_tups = [
+        locs = [
             ("initial_states", 0, f"mixture_{emf}", factors[0])
             for emf in range(n_mixtures)
         ]
-        constr = [{"loc": ind_tups, "type": "increasing", "description": msg}]
+        constraints_dicts = [{"loc": locs, "type": "increasing", "description": msg}]
     else:
-        constr = []
+        constraints_dicts = []
 
-    return constr
+    return constraints_dicts
 
 
-def _get_transition_constraints(labels):
+def _get_transition_constraints(labels) -> list[dict]:
     """Collect possible constraints on transition parameters.
 
     Args:
@@ -269,10 +282,10 @@ def _get_transition_constraints(labels):
             factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     Returns:
-        constraints (list)
+        constraints_dicts
 
     """
-    constraints = []
+    constraints_dicts = []
     for f, factor in enumerate(labels["latent_factors"]):
         tname = labels["transition_names"][f]
         msg = f"This constraint is inherent to the {tname} production function."
@@ -280,14 +293,16 @@ def _get_transition_constraints(labels):
             funcname = f"constraints_{tname}"
             if hasattr(tf, funcname):
                 func = getattr(tf, funcname)
-                constr = func(factor, labels["all_factors"], period)
-                if "description" not in constr:
-                    constr["description"] = msg
-                constraints.append(constr)
-    return constraints
+                c = func(factor, labels["all_factors"], period)
+                if "description" not in c:
+                    c["description"] = msg
+                constraints_dicts.append(c)
+    return constraints_dicts
 
 
-def _get_anchoring_constraints(update_info, controls, anchoring_info, periods):
+def _get_anchoring_constraints(
+    update_info, controls, anchoring_info, periods
+) -> list[dict]:
     """Constraints on anchoring parameters.
 
     Args:
@@ -298,12 +313,12 @@ def _get_anchoring_constraints(update_info, controls, anchoring_info, periods):
         periods (list): Period of the model
 
     Returns:
-        constraints (list)
+        constraints_dicts
 
     """
     anchoring_updates = update_info[update_info["purpose"] == "anchoring"].index
 
-    constraints = []
+    constraints_dicts = []
     if not anchoring_info["free_constant"]:
         msg = (
             "This constraint was generated because free_constant in the anchoring "
@@ -312,7 +327,7 @@ def _get_anchoring_constraints(update_info, controls, anchoring_info, periods):
         locs = []
         for period, meas in anchoring_updates:
             locs.append(("controls", period, meas, "constant"))
-        constraints.append(
+        constraints_dicts.append(
             {"loc": locs, "type": "fixed", "value": 0, "description": msg},
         )
 
@@ -325,7 +340,7 @@ def _get_anchoring_constraints(update_info, controls, anchoring_info, periods):
         for period, meas in anchoring_updates:
             for cont in [c for c in controls if c != "constant"]:
                 ind_tups.append(("controls", period, meas, cont))
-        constraints.append(
+        constraints_dicts.append(
             {"loc": ind_tups, "type": "fixed", "value": 0, "description": msg},
         )
 
@@ -341,10 +356,86 @@ def _get_anchoring_constraints(update_info, controls, anchoring_info, periods):
                 meas = f"{outcome}_{factor}"
                 ind_tups.append(("loadings", period, meas, factor))
 
-        constraints.append(
+        constraints_dicts.append(
             {"loc": ind_tups, "type": "fixed", "value": 1, "description": msg},
         )
 
-    constraints = [c for c in constraints if c["loc"] != []]
+    constraints_dicts = [c for c in constraints_dicts if c["loc"] != []]
+
+    return constraints_dicts
+
 
-    return constraints
+def _sel(params, loc):
+    return params.loc[loc]
+
+
+def constraints_dicts_to_om(
+    constraints_dicts: list[dict],
+) -> list[om.constraints.Constraint]:
+    """Convert constraints provided in dictionary form to optimagic constraints.
+
+    Each dictionary requires an
+
+    Args:
+        constraints_dicts (list): see :ref:`get_constraints_dicts`.
+
+    Returns:
+        List of optimagic constraints.
+    """
+    om_style = []
+    for c_d in constraints_dicts:
+        if c_d["type"] == "pairwise_equality":
+            om_style.append(
+                om.PairwiseEqualityConstraint(
+                    selectors=[functools.partial(_sel, loc=loc) for loc in c_d["locs"]]
+                )
+            )
+        else:
+            sel = functools.partial(_sel, loc=c_d["loc"])
+            if c_d["type"] == "fixed":
+                om_style.append(om.FixedConstraint(selector=sel))
+            elif c_d["type"] == "equality":
+                om_style.append(om.EqualityConstraint(selector=sel))
+            elif c_d["type"] == "probability":
+                om_style.append(om.ProbabilityConstraint(selector=sel))
+            elif c_d["type"] == "increasing":
+                om_style.append(om.IncreasingConstraint(selector=sel))
+            else:
+                raise TypeError(c_d["type"])
+    return om_style
+
+
+def enforce_fixed_constraints(
+    params_template: pd.DataFrame,
+    constraints_dicts: list[dict[str, Any]],
+) -> pd.DataFrame:
+    """Enforce fixed constraints on params_template.
+
+    For fixed constraints, we also set the lower and upper bounds to the fixed value.
+    This means that any robust bounds will be overridden for fixed parameters.
+
+    Args:
+        params_template (pd.DataFrame): see :ref:`params_df`.
+        constraints_dicts (list): see :ref:`get_constraints_dicts`.
+
+    Returns:
+        pd.DataFrame: modified copy of params_template
+    """
+    params = params_template.copy()
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            message="indexing past lexsort depth may impact performance.",
+        )
+        for constraint in constraints_dicts:
+            if constraint["type"] == "fixed":
+                params.loc[constraint["loc"], "value"] = constraint["value"]
+                params.loc[constraint["loc"], "lower_bound"] = constraint["value"]
+                params.loc[constraint["loc"], "upper_bound"] = constraint["value"]
+
+    # Check that fixed constraints are valid
+    fixed = params[params["value"].notna()]
+    invalid = fixed.query("value < lower_bound or value > upper_bound")
+    if len(invalid) > 0:
+        raise ValueError(f"Invalid fixed constraints:\n\n{invalid}")
+    return params
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 1e446b5..398b003 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -1,15 +1,18 @@
 import functools
-from typing import Any
 
 import jax
 import jax.numpy as jnp
 import numpy as np
-import optimagic as om
 import pandas as pd
 
 import skillmodels.likelihood_function as lf
 import skillmodels.likelihood_function_debug as lfd
-from skillmodels.constraints import add_bounds, get_constraint_tuples
+from skillmodels.constraints import (
+    add_bounds,
+    constraints_dicts_to_om,
+    enforce_fixed_constraints,
+    get_constraints_dicts,
+)
 from skillmodels.kalman_filters import calculate_sigma_scaling_factor_and_weights
 from skillmodels.params_index import get_params_index
 from skillmodels.parse_params import create_parsing_info
@@ -43,7 +46,8 @@ def get_maximization_inputs(model_dict, data):
         constraints (list): List of optimagic constraints that are implied by the
             model specification.
         params_template (pd.DataFrame): Parameter DataFrame with correct index and
-            bounds. The value column is empty except for the fixed constraints.
+            bounds. The value column is empty except for the fixed constraints, which
+            are set including the bounds.
         data_aug (pd.DataFrame): DataFrame with augmented data. If model contains
             investment factors, we double up the number of periods in order to add
 
@@ -125,7 +129,7 @@ def debug_loglike(params):
         tmp["value"] = float(tmp["value"])
         return process_debug_data(debug_data=tmp, model=model)
 
-    _constraints_tuples = get_constraint_tuples(
+    _constraints_tuples = get_constraints_dicts(
         dimensions=model["dimensions"],
         labels=model["labels"],
         anchoring_info=model["anchoring"],
@@ -133,16 +137,16 @@ def debug_loglike(params):
         normalizations=model["normalizations"],
     )
 
-    constraints = convert_old_style_constraints(_constraints_tuples)
+    constraints = constraints_dicts_to_om(_constraints_tuples)
 
     params_template = pd.DataFrame(columns=["value"], index=p_index)
     params_template = add_bounds(
-        params_template,
-        model["estimation_options"]["bounds_distance"],
+        params=params_template,
+        bounds_distance=model["estimation_options"]["bounds_distance"],
     )
-    params_template = _fill_fixed_constraints(
+    params_template = enforce_fixed_constraints(
         params_template=params_template,
-        constraints_tuples=_constraints_tuples,
+        constraints_dicts=_constraints_tuples,
     )
 
     out = {
@@ -226,49 +230,3 @@ def _get_jnp_params_vec(params, target_index):
 
     vec = jnp.array(params.reindex(target_index)["value"].to_numpy())
     return vec
-
-
-def _sel(params, loc):
-    return params.loc[loc]
-
-
-def convert_old_style_constraints(old_style):
-    # Need this in many cases, anyhow -- so just impose to simplify code below!
-    new_style = []
-    for oc in old_style:
-        if oc["type"] == "pairwise_equality":
-            new_style.append(
-                om.PairwiseEqualityConstraint(
-                    selectors=[functools.partial(_sel, loc=loc) for loc in oc["locs"]]
-                )
-            )
-        else:
-            sel = functools.partial(_sel, loc=oc["loc"])
-            if oc["type"] == "fixed":
-                new_style.append(om.FixedConstraint(selector=sel))
-            elif oc["type"] == "equality":
-                new_style.append(om.EqualityConstraint(selector=sel))
-            elif oc["type"] == "probability":
-                new_style.append(om.ProbabilityConstraint(selector=sel))
-            elif oc["type"] == "increasing":
-                new_style.append(om.IncreasingConstraint(selector=sel))
-            else:
-                raise TypeError(oc["type"])
-    return new_style
-
-
-def _fill_fixed_constraints(
-    params_template: pd.DataFrame,
-    constraints_tuples: list[dict[str, Any]],
-) -> pd.DataFrame:
-    params = params_template.copy()
-    for constr in constraints_tuples:
-        if constr["type"] == "fixed":
-            params.loc[constr["loc"], "value"] = constr["value"]
-
-    # Check that fixed constraints are valid
-    fixed = params[params["value"].notna()]
-    invalid = fixed.query("value < lower_bound or value > upper_bound")
-    if len(invalid) > 0:
-        raise ValueError(f"Invalid fixed constraints:\n\n{invalid}")
-    return params
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index e647e0c..9328624 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -50,15 +50,29 @@ def test_normalization_constraints():
 
     expected = [
         {
-            "loc": [
-                ("loadings", 0, "m1", "fac1"),
-                ("loadings", 0, "m2", "fac1"),
-                ("controls", 0, "m1", "constant"),
-                ("loadings", 1, "m1", "fac1"),
-                ("loadings", 0, "m3", "fac2"),
-            ],
+            "loc": ("loadings", 0, "m1", "fac1"),
+            "type": "fixed",
+            "value": 2,
+        },
+        {
+            "loc": ("loadings", 0, "m2", "fac1"),
+            "type": "fixed",
+            "value": 1.5,
+        },
+        {
+            "loc": ("controls", 0, "m1", "constant"),
             "type": "fixed",
-            "value": [2, 1.5, 0.5, 3, 1],
+            "value": 0.5,
+        },
+        {
+            "loc": ("loadings", 1, "m1", "fac1"),
+            "type": "fixed",
+            "value": 3,
+        },
+        {
+            "loc": ("loadings", 0, "m3", "fac2"),
+            "type": "fixed",
+            "value": 1,
         },
     ]
 
@@ -266,8 +280,8 @@ def test_anchoring_constraints_for_controls(anch_uinfo, base_anchoring_info):
         (0, 1),
     )
 
-    for constr in calculated:
-        del constr["description"]
+    for c_t in calculated:
+        del c_t["description"]
 
     expected = [
         {
@@ -306,8 +320,8 @@ def test_anchoring_constraints_for_loadings(anch_uinfo, base_anchoring_info):
         },
     ]
 
-    for constr in calculated:
-        del constr["description"]
+    for c_t in calculated:
+        del c_t["description"]
 
     assert calculated == expected
 

From b575605652e9d564aaf38baf9be73c52dc031907 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Wed, 26 Feb 2025 11:07:45 +0100
Subject: [PATCH 16/48] Refactor test for augmented data so we can re-use the
 fixture.

---
 tests/simplest_augmented_data_expected.csv |  13 ++
 tests/simplest_augmented_model.yaml        |  19 +++
 tests/test_process_data.py                 | 155 ++++-----------------
 3 files changed, 56 insertions(+), 131 deletions(-)
 create mode 100644 tests/simplest_augmented_data_expected.csv
 create mode 100644 tests/simplest_augmented_model.yaml

diff --git a/tests/simplest_augmented_data_expected.csv b/tests/simplest_augmented_data_expected.csv
new file mode 100644
index 0000000..5065e5c
--- /dev/null
+++ b/tests/simplest_augmented_data_expected.csv
@@ -0,0 +1,13 @@
+id,period,period_raw,constant,var,inv,of
+0,0,0,1,0.0,,10.0
+0,1,0,1,,5.0,10.0
+0,2,1,1,,,
+0,3,1,1,,,
+1,0,0,1,1.0,,11.0
+1,1,0,1,,6.0,11.0
+1,2,1,1,2.0,,12.0
+1,3,1,1,,7.0,12.0
+2,0,0,1,3.0,,13.0
+2,1,0,1,,8.0,13.0
+2,2,1,1,4.0,,14.0
+2,3,1,1,,9.0,14.0
diff --git a/tests/simplest_augmented_model.yaml b/tests/simplest_augmented_model.yaml
new file mode 100644
index 0000000..9b6b442
--- /dev/null
+++ b/tests/simplest_augmented_model.yaml
@@ -0,0 +1,19 @@
+---
+factors:
+  fac1:
+    is_correction: false
+    is_investment: false
+    measurements:
+      -   - var
+      -   - var
+    transition_function: linear
+  fac2:
+    is_correction: false
+    is_investment: true
+    measurements:
+      -   - inv
+      -   - inv
+    transition_function: linear
+observed_factors:
+  - of
+estimation_options: {}
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index 34678fb..ffb7e6a 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -1,10 +1,12 @@
 import io
 import textwrap
+from pathlib import Path
 
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
 import pytest
+import yaml
 from numpy.testing import assert_array_equal as aae
 
 from skillmodels.process_data import (
@@ -17,6 +19,9 @@
 )
 from skillmodels.process_model import process_model
 
+# importing the TEST_DIR from config does not work for test run in conda build
+TEST_DIR = Path(__file__).parent.resolve()
+
 
 def test_pre_process_data():
     df = pd.DataFrame(data=np.arange(20).reshape(2, 10).T, columns=["var", "inv"])
@@ -40,146 +45,34 @@ def test_pre_process_data():
     assert res[["var", "inv"]].equals(exp[["var", "inv"]])
 
 
-def test_augment_data_for_investments():
-    df = pd.DataFrame(data=np.arange(15).reshape(3, 5).T, columns=["var", "inv", "of"])
-    df["period"] = [1, 1, 2, 1, 2]
-    df["id"] = [1, 3, 3, 5, 5]
-    df.set_index(["id", "period"], inplace=True)
+@pytest.fixture
+def simplest_augmented():
+    out = {}
+    with open(TEST_DIR / "simplest_augmented_model.yaml") as y:
+        out["model_dict"] = yaml.load(y, Loader=yaml.FullLoader)
+    _df = pd.DataFrame(data=np.arange(15).reshape(3, 5).T, columns=["var", "inv", "of"])
+    _df["period"] = [1, 1, 2, 1, 2]
+    _df["id"] = [1, 3, 3, 5, 5]
+    out["data_input"] = _df.set_index(["id", "period"])
+    out["data_exp"] = pd.read_csv(
+        TEST_DIR / "simplest_augmented_data_expected.csv", index_col=["id", "period"]
+    )
+    return out
 
-    model_dict = {
-        "factors": {
-            "fac1": {
-                "measurements": [["var"], ["var"]],
-                "transition_function": "linear",
-                "is_investment": False,
-                "is_correction": False,
-            },
-            "fac2": {
-                "measurements": [["inv"], ["inv"]],
-                "transition_function": "linear",
-                "is_investment": True,
-                "is_correction": False,
-            },
-        },
-        "observed_factors": ["of"],
-        "estimation_options": {},
-    }
 
-    model = process_model(model_dict)
-    pre_processed_data = pre_process_data(df, model["labels"]["periods_raw"])
+def test_augment_data_for_investments(simplest_augmented):
+    model = process_model(simplest_augmented["model_dict"])
+    pre_processed_data = pre_process_data(
+        simplest_augmented["data_input"], model["labels"]["periods_raw"]
+    )
     pre_processed_data["constant"] = 1
-
-    nan = np.nan
-    exp = pd.DataFrame(
-        {
-            "id": {
-                0: 0,
-                1: 0,
-                2: 0,
-                3: 0,
-                4: 1,
-                5: 1,
-                6: 1,
-                7: 1,
-                8: 2,
-                9: 2,
-                10: 2,
-                11: 2,
-            },
-            "period": {
-                0: 0,
-                1: 1,
-                2: 2,
-                3: 3,
-                4: 0,
-                5: 1,
-                6: 2,
-                7: 3,
-                8: 0,
-                9: 1,
-                10: 2,
-                11: 3,
-            },
-            "period_raw": {
-                0: 0,
-                1: 0,
-                2: 1,
-                3: 1,
-                4: 0,
-                5: 0,
-                6: 1,
-                7: 1,
-                8: 0,
-                9: 0,
-                10: 1,
-                11: 1,
-            },
-            "constant": {
-                0: 1,
-                1: 1,
-                2: 1,
-                3: 1,
-                4: 1,
-                5: 1,
-                6: 1,
-                7: 1,
-                8: 1,
-                9: 1,
-                10: 1,
-                11: 1,
-            },
-            "var": {
-                0: 0.0,
-                1: nan,
-                2: nan,
-                3: nan,
-                4: 1.0,
-                5: nan,
-                6: 2.0,
-                7: nan,
-                8: 3.0,
-                9: nan,
-                10: 4.0,
-                11: nan,
-            },
-            "inv": {
-                0: nan,
-                1: 5.0,
-                2: nan,
-                3: nan,
-                4: nan,
-                5: 6.0,
-                6: nan,
-                7: 7.0,
-                8: nan,
-                9: 8.0,
-                10: nan,
-                11: 9.0,
-            },
-            "of": {
-                0: 10.0,
-                1: 10.0,
-                2: nan,
-                3: nan,
-                4: 11.0,
-                5: 11.0,
-                6: 12.0,
-                7: 12.0,
-                8: 13.0,
-                9: 13.0,
-                10: 14.0,
-                11: 14.0,
-            },
-        }
-    ).set_index(["id", "period"])
-
     res = _augment_data_for_investments(
         df=pre_processed_data,
         labels=model["labels"],
         update_info=model["update_info"],
     )
     cols = ["period_raw", "var", "inv", "constant", "of"]
-    pd.testing.assert_frame_equal(res[cols], exp[cols])
+    pd.testing.assert_frame_equal(res[cols], simplest_augmented["data_exp"][cols])
 
 
 def test_handle_controls_with_missings():

From d8ce0b6faa5988fcd6680bbf7db50972ae7a9a9d Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Thu, 27 Feb 2025 14:35:07 +0100
Subject: [PATCH 17/48] Implement restrictions for augmented periods. Allow
 stages + investments.

---
 docs/source/getting_started/tutorial.ipynb    |   7 +
 src/skillmodels/check_model.py                |  34 +++--
 src/skillmodels/constraints.py                | 104 +++++++++++---
 src/skillmodels/maximization_inputs.py        |   3 +-
 src/skillmodels/process_data.py               |  16 ++-
 src/skillmodels/process_model.py              | 134 ++++++++++++------
 src/skillmodels/simulate_data.py              |   2 +-
 src/skillmodels/transition_functions.py       |  60 ++++++++
 .../visualize_transition_equations.py         |   2 +-
 tests/simplest_augmented_model.yaml           |   8 ++
 tests/test_constraints.py                     |  80 ++++++++++-
 tests/test_process_model.py                   |  31 ++--
 12 files changed, 382 insertions(+), 99 deletions(-)

diff --git a/docs/source/getting_started/tutorial.ipynb b/docs/source/getting_started/tutorial.ipynb
index 60cd035..7d46c54 100644
--- a/docs/source/getting_started/tutorial.ipynb
+++ b/docs/source/getting_started/tutorial.ipynb
@@ -23,6 +23,13 @@
     "from skillmodels.maximization_inputs import get_maximization_inputs"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/src/skillmodels/check_model.py b/src/skillmodels/check_model.py
index 6d0ab20..f866e15 100644
--- a/src/skillmodels/check_model.py
+++ b/src/skillmodels/check_model.py
@@ -25,10 +25,11 @@ def check_model(model_dict, labels, dimensions, anchoring, has_investments):
         ValueError
 
     """
-    report = _check_stagemap(
-        labels["stagemap"],
-        labels["stages"],
-        dimensions["n_periods"],
+    report = check_stagemap(
+        stagemap=labels["stagemap"],
+        stages=labels["stages"],
+        n_periods=dimensions["n_periods"],
+        has_investments=has_investments,
     )
     report += _check_anchoring(anchoring)
     invalid_measurements = _check_measurements(model_dict, labels["latent_factors"])
@@ -43,22 +44,27 @@ def check_model(model_dict, labels, dimensions, anchoring, has_investments):
 
     report = "\n".join(report)
     if report != "":
-        raise ValueError(f"Invalid model specification:\n{report}")
+        raise ValueError(f"Invalid model specification: {report}")
 
 
-def _check_stagemap(stagemap, stages, n_periods):
+def check_stagemap(stagemap, stages, n_periods, has_investments):
     report = []
-    if len(stagemap) != n_periods - 1:
+    len_reduction = 2 if has_investments else 1
+    if len(stagemap) != n_periods - len_reduction:
         report.append(
-            "The stagemap needs to be of length n_periods - 1. n_periods is "
-            f"{n_periods}, the stagemap has length {len(stagemap)}.",
+            f"The stagemap needs to be of length n_periods - {len_reduction}. "
+            f" n_periods is {n_periods}, the stagemap has length {len(stagemap)}.",
         )
+    if not has_investments:
+        if stages != list(range(len(stages))):
+            report.append(
+                "Stages need to be integers, start at zero and increase by 1."
+            )
 
-    if stages != list(range(len(stages))):
-        report.append("Stages need to be integers, start at zero and increase by 1.")
-
-    if not np.isin(np.array(stagemap[1:]) - np.array(stagemap[:-1]), (0, 1)).all():
-        report.append("Consecutive entries in stagemap must be equal or increase by 1.")
+        if not np.isin(np.array(stagemap[1:]) - np.array(stagemap[:-1]), (0, 1)).all():
+            report.append(
+                "Consecutive entries in stagemap must be equal or increase by 1."
+            )
     return report
 
 
diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index f939022..d4f8c6a 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -8,11 +8,11 @@
 import optimagic as om
 import pandas as pd
 
-import skillmodels.transition_functions as tf
+import skillmodels.transition_functions as t_f_module
 
 
 def get_constraints_dicts(
-    dimensions, labels, anchoring_info, update_info, normalizations
+    dimensions, labels, anchoring_info, update_info, normalizations, investments_info
 ) -> list[dict]:
     """Generate constraints implied by the model specification.
 
@@ -31,7 +31,7 @@ def get_constraints_dicts(
             loadings and intercepts for each factor. See :ref:`normalizations`.
 
     Returns:
-        A list of constraints with entries:
+        A list of constraints dictionaries with entries:
         - "type": str, one of "fixed", "equality", "probability", "increasing",
             "pairwise_equality". Must map to an optimagic constraint, see
             :func:`constraints_dicts_to_om`.
@@ -40,28 +40,38 @@ def get_constraints_dicts(
         - "description": str, optional description of the constraint
 
     """
-    c_d = []
-
-    c_d += _get_normalization_constraints(normalizations, labels["latent_factors"])
-    c_d += _get_mixture_weights_constraints(dimensions["n_mixtures"])
-    c_d += _get_stage_constraints(labels["stagemap"], labels["stages"])
-    c_d += _get_constant_factors_constraints(labels)
-    c_d += _get_initial_states_constraints(
-        dimensions["n_mixtures"],
-        labels["latent_factors"],
+    constraints_dicts = []
+
+    constraints_dicts += _get_normalization_constraints(
+        normalizations, labels["latent_factors"]
+    )
+    constraints_dicts += _get_mixture_weights_constraints(dimensions["n_mixtures"])
+    constraints_dicts += _get_stage_constraints(
+        stagemap=labels["stagemap"],
+        stages=labels["stages"],
+    )
+    constraints_dicts += _get_constant_factors_constraints(labels=labels)
+    constraints_dicts += _get_initial_states_constraints(
+        n_mixtures=dimensions["n_mixtures"],
+        factors=labels["latent_factors"],
     )
-    c_d += _get_transition_constraints(labels)
-    c_d += _get_anchoring_constraints(
-        update_info,
-        labels["controls"],
-        anchoring_info,
-        labels["periods"],
+    constraints_dicts += _get_transition_constraints(labels=labels)
+    constraints_dicts += _get_anchoring_constraints(
+        update_info=update_info,
+        controls=labels["controls"],
+        anchoring_info=anchoring_info,
+        periods=labels["periods"],
     )
+    if investments_info["has_investments"]:
+        constraints_dicts += _get_constraints_for_augmented_periods(
+            labels=labels,
+            investments_info=investments_info,
+        )
 
-    for i, c in enumerate(c_d):
+    for i, c in enumerate(constraints_dicts):
         c["id"] = i
 
-    return c_d
+    return constraints_dicts
 
 
 def add_bounds(params: pd.DataFrame, bounds_distance: float) -> pd.DataFrame:
@@ -291,9 +301,8 @@ def _get_transition_constraints(labels) -> list[dict]:
         msg = f"This constraint is inherent to the {tname} production function."
         for period in labels["periods"][:-1]:
             funcname = f"constraints_{tname}"
-            if hasattr(tf, funcname):
-                func = getattr(tf, funcname)
-                c = func(factor, labels["all_factors"], period)
+            if func := getattr(t_f_module, funcname, False):
+                c = func(factor=factor, factors=labels["all_factors"], period=period)
                 if "description" not in c:
                     c["description"] = msg
                 constraints_dicts.append(c)
@@ -365,6 +374,55 @@ def _get_anchoring_constraints(
     return constraints_dicts
 
 
+def _get_constraints_for_augmented_periods(labels, investments_info) -> list[dict]:
+    """Constraints for augmented periods.
+
+    - Carry forward states from uneven periods to even periods
+    - Carry forward investments even periods to uneven periods
+    - Set shock_sds to 0 when carrying anything forward
+
+    Both depend on the transition function.
+
+    Args:
+        labels (dict): Dict of lists with labels for the model quantities like
+            factors, periods, controls, stagemap and stages. See :ref:`labels`
+
+    Returns:
+        constraints_dicts
+
+    """
+    constraints_dicts = []
+    for f, factor in enumerate(labels["latent_factors"]):
+        tname = labels["transition_names"][f]
+        if tname == "constant":
+            continue
+        period_type_to_constrain = (
+            "investments" if investments_info[factor]["is_state"] else "states"
+        )
+        periods_to_constrain = [
+            k
+            for k, v in investments_info["periods_to_period_types"].items()
+            if v == period_type_to_constrain
+        ]
+        for period in periods_to_constrain:
+            if func := getattr(t_f_module, f"identity_constraints_{tname}", False):
+                constraints_dicts += func(
+                    factor=factor,
+                    period=period,
+                    all_factors=labels["all_factors"],
+                )
+            constraints_dicts.append(
+                {
+                    "loc": ("shock_sds", period, factor, "-"),
+                    "type": "fixed",
+                    "value": 0.0,
+                    "description": "Identity constraint.",
+                }
+            )
+
+    return constraints_dicts
+
+
 def _sel(params, loc):
     return params.loc[loc]
 
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 398b003..397d308 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -70,7 +70,7 @@ def get_maximization_inputs(model_dict, data):
     )
     processed_data = process_data(
         df=data,
-        has_investments=model["has_investments"],
+        has_investments=model["investments_info"]["has_investments"],
         labels=model["labels"],
         update_info=model["update_info"],
         anchoring_info=model["anchoring"],
@@ -135,6 +135,7 @@ def debug_loglike(params):
         anchoring_info=model["anchoring"],
         update_info=model["update_info"],
         normalizations=model["normalizations"],
+        investments_info=model["investments_info"],
     )
 
     constraints = constraints_dicts_to_om(_constraints_tuples)
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 78cbaf6..a79759d 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -5,8 +5,6 @@
 import numpy as np
 import pandas as pd
 
-from skillmodels.process_model import get_period_measurements
-
 
 def process_data(
     df, has_investments, labels, update_info, anchoring_info, purpose="estimation"
@@ -94,7 +92,7 @@ def _get_period_data_for_investments(
     labels: dict[str, Any],
     update_info: pd.DataFrame,
 ) -> pd.DataFrame:
-    meas = get_period_measurements(update_info, period)
+    meas = _get_period_measurements(update_info, period)
     controls = labels["controls"]
     observed = labels["observed_factors"]
 
@@ -162,7 +160,7 @@ def _check_data(df, update_info, labels, purpose):  # noqa: C901
                 var_report.loc[(period, cont), "problem"] = "Variable is missing"
 
         if purpose == "estimation":
-            for meas in get_period_measurements(update_info, period):
+            for meas in _get_period_measurements(update_info, period):
                 if meas not in period_data.columns:
                     var_report.loc[(period, meas), "problem"] = "Variable is missing"
                 elif len(period_data[meas].dropna().unique()) == 1:
@@ -188,7 +186,7 @@ def _handle_controls_with_missings(df, controls, update_info):
     for period in periods:
         period_data = df.query(f"period == {period}")
         control_data = period_data[controls]
-        meas_data = period_data[get_period_measurements(update_info, period)]
+        meas_data = period_data[_get_period_measurements(update_info, period)]
         problem = control_data.isna().any(axis=1) & meas_data.notna().any(axis=1)
         problematic_index = problematic_index.union(period_data[problem].index)
 
@@ -201,6 +199,14 @@ def _handle_controls_with_missings(df, controls, update_info):
     return df
 
 
+def _get_period_measurements(update_info, period):
+    if period in update_info.index:
+        measurements = list(update_info.loc[period].index)
+    else:
+        measurements = []
+    return measurements
+
+
 def _generate_measurements_array(df, update_info, n_obs):
     arr = np.zeros((len(update_info), n_obs))
     for k, (period, var) in enumerate(update_info.index):
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 4bf4689..25b5d88 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -9,8 +9,8 @@
 from jax import vmap
 from pandas import DataFrame
 
-import skillmodels.transition_functions as tf
-from skillmodels.check_model import check_model
+import skillmodels.transition_functions as t_f_module
+from skillmodels.check_model import check_model, check_stagemap
 from skillmodels.decorators import extract_params, jax_array_output
 
 pd.set_option("future.no_silent_downcasting", True)  # noqa:  FBT003
@@ -29,37 +29,48 @@ def process_model(model_dict):
     Returns:
         dict: nested dictionary of model specs. It has the following entries:
         - dimensions (dict): Dimensional information like n_states, n_periods,
-        n_controls, n_mixtures. See :ref:`dimensions`.
+          n_controls, n_mixtures. See :ref:`dimensions`.
         - labels (dict): Dict of lists with labels for the model quantities like
-        factors, periods, controls, stagemap and stages. See :ref:`labels`
+          factors, periods, controls, stagemap and stages. See :ref:`labels`
         - anchoring (dict): Information about anchoring. See :ref:`anchoring`
         - transition_info (dict): Everything related to transition functions.
         - update_info (pandas.DataFrame): DataFrame with one row per Kalman update
-        needed in the likelihood function. See :ref:`update_info`.
+          needed in the likelihood function. See :ref:`update_info`.
         - normalizations (dict): Nested dictionary with information on normalized factor
-        loadings and intercepts for each factor. See :ref:`normalizations`.
+          loadings and intercepts for each factor. See :ref:`normalizations`.
 
     """
     has_investments = get_has_investments(model_dict["factors"])
-    dims = get_dimensions(model_dict, has_investments)
+    dims = get_dimensions(model_dict=model_dict, has_investments=has_investments)
     labels = _get_labels(
         model_dict=model_dict, has_investments=has_investments, dimensions=dims
     )
     anchoring = _process_anchoring(model_dict, has_investments)
-    check_model(model_dict, labels, dims, anchoring, has_investments)
     if has_investments:
         model_dict_aug = _augment_periods_for_investments(
             model_dict=model_dict,
             dimensions=dims,
             labels=labels,
         )
+        investments_info = _get_investments_info(
+            has_investments=has_investments,
+            model_dict=model_dict_aug,
+            labels=labels,
+        )
     else:
         model_dict_aug = model_dict
+        investments_info = {"has_investments": has_investments}
+    check_model(
+        model_dict=model_dict_aug,
+        labels=labels,
+        dimensions=dims,
+        anchoring=anchoring,
+        has_investments=has_investments,
+    )
     transition_info = _get_transition_info(model_dict_aug, labels)
     labels["transition_names"] = list(transition_info["function_names"].values())
 
     processed = {
-        "has_investments": has_investments,
         "dimensions": dims,
         "labels": labels,
         "anchoring": anchoring,
@@ -67,6 +78,7 @@ def process_model(model_dict):
         "transition_info": transition_info,
         "update_info": _get_update_info(model_dict_aug, dims, labels, anchoring),
         "normalizations": _process_normalizations(model_dict_aug, dims, labels),
+        "investments_info": investments_info,
     }
     return processed
 
@@ -125,24 +137,13 @@ def get_dimensions(model_dict, has_investments):
 
 
 def _get_periods_to_periods_raw(
-    dimensions: dict[str, int], has_investments: bool
+    n_periods: int, has_investments: bool
 ) -> dict[int, int]:
     """Return mapper of periods potentially augmented for investments to raw periods."""
-    periods = list(range(dimensions["n_periods"]))
+    periods = list(range(n_periods))
     return {p: p // 2 for p in periods} if has_investments else {p: p for p in periods}
 
 
-def _get_periods_to_period_types(
-    periods: list[int], has_investments: bool
-) -> dict[int, Literal["states", "investments"]]:
-    return {
-        p: ("states" if p % 2 == 0 else "investments")
-        if has_investments
-        else {p: "states"}
-        for p in periods
-    }
-
-
 def _get_labels(model_dict, has_investments, dimensions):
     """Extract labels of the model quantities.
 
@@ -157,15 +158,37 @@ def _get_labels(model_dict, has_investments, dimensions):
         factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     """
-    if has_investments and "stagemap" in model_dict:
-        raise ValueError("Stages currently not supported when investments are present.")
-
-    stagemap = model_dict.get("stagemap", list(range(dimensions["n_periods"] - 1)))
+    periods_to_periods_raw = _get_periods_to_periods_raw(
+        n_periods=dimensions["n_periods"],
+        has_investments=has_investments,
+    )
 
-    periods_to_periods_raw = _get_periods_to_periods_raw(dimensions, has_investments)
-    periods_to_period_types = _get_periods_to_period_types(
-        list(periods_to_periods_raw.keys()), has_investments
+    stagemap_raw = model_dict.get(
+        "stagemap", list(range(dimensions["n_periods_raw"] - 1))
     )
+    stages_raw = sorted(int(v) for v in np.unique(stagemap_raw))
+
+    if has_investments:
+        report = check_stagemap(
+            stagemap=stagemap_raw,
+            stages=stages_raw,
+            n_periods=dimensions["n_periods_raw"],
+            has_investments=False,
+        )
+        if report:
+            raise ValueError(f"Invalid stage map: {report}")
+        stagemap = []
+        stages_to_stages_raw = {}
+        relevant_periods = sorted(periods_to_periods_raw.keys())[:-2]
+        for p in relevant_periods:
+            p_raw = periods_to_periods_raw[p]
+            s_raw = stagemap_raw[p_raw]
+            s = 2 * s_raw + p % 2
+            stagemap.append(s)
+            stages_to_stages_raw[s] = s_raw
+    else:
+        stagemap = stagemap_raw
+        stages_to_stages_raw = {s_raw: s_raw for s_raw in stages_raw}
 
     labels = {
         "latent_factors": list(model_dict["factors"]),
@@ -174,9 +197,11 @@ def _get_labels(model_dict, has_investments, dimensions):
         "periods": list(periods_to_periods_raw.keys()),
         "periods_raw": sorted(set(periods_to_periods_raw.values())),
         "periods_to_periods_raw": periods_to_periods_raw,
-        "periods_to_period_types": periods_to_period_types,
         "stagemap": stagemap,
-        "stages": sorted(np.unique(stagemap)),
+        "stages": sorted(int(v) for v in np.unique(stagemap)),
+        "stages_to_stages_raw": stages_to_stages_raw,
+        "stagemap_raw": stagemap_raw,
+        "stages_raw": stages_raw,
     }
 
     labels["all_factors"] = labels["latent_factors"] + labels["observed_factors"]
@@ -310,11 +335,11 @@ def _get_transition_info(model_dict, labels):
     for factor in latent_factors:
         spec = model_dict["factors"][factor]["transition_function"]
         if isinstance(spec, str):
-            func = getattr(tf, spec)
+            func = getattr(t_f_module, spec)
             if spec == "constant":
                 func = rename_arguments(func, mapper={"state": factor})
             func_list.append(extract_params(func, key=factor))
-            param_names.append(getattr(tf, f"params_{spec}")(all_factors))
+            param_names.append(getattr(t_f_module, f"params_{spec}")(all_factors))
         elif callable(spec):
             if not hasattr(spec, "__name__"):
                 raise AttributeError(
@@ -369,6 +394,41 @@ def _extract_factor(states, pos):
     return out
 
 
+def _get_investments_info(
+    has_investments: bool,
+    model_dict: dict[str, Any],
+    labels: dict[str, Any],
+) -> dict[str, Any]:
+    """Collect information about investments."""
+    investments_info = {
+        "has_investments": has_investments,
+        "periods_to_period_types": _get_periods_to_period_types(
+            periods=labels["periods_to_periods_raw"].keys(),
+            has_investments=has_investments,
+        ),
+    }
+    for fac, v in model_dict["factors"].items():
+        investments_info[fac] = {
+            "is_state": (
+                not v.get("is_investment", False) and not v.get("is_correction", False)
+            ),
+            "is_investment": v.get("is_investment", False),
+            "is_correction": v.get("is_correction", False),
+        }
+    return investments_info
+
+
+def _get_periods_to_period_types(
+    periods: list[int], has_investments: bool
+) -> dict[int, Literal["states", "investments"]]:
+    return {
+        p: ("states" if p % 2 == 0 else "investments")
+        if has_investments
+        else {p: "states"}
+        for p in periods
+    }
+
+
 def _get_update_info(model_dict, dimensions, labels, anchoring_info):
     """Construct a DataFrame with information on each Kalman update.
 
@@ -444,11 +504,3 @@ def _process_normalizations(model_dict, dimensions, labels):
             normalizations[factor][norm_type] = candidate
 
     return normalizations
-
-
-def get_period_measurements(update_info, period):
-    if period in update_info.index:
-        measurements = list(update_info.loc[period].index)
-    else:
-        measurements = []
-    return measurements
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index 59c2028..a84df7e 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -52,7 +52,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
     if data is not None:
         processed_data = process_data(
             df=data,
-            has_investments=model["has_investments"],
+            has_investments=model["investments_info"]["has_investments"],
             labels=model["labels"],
             update_info=model["update_info"],
             anchoring_info=model["anchoring"],
diff --git a/src/skillmodels/transition_functions.py b/src/skillmodels/transition_functions.py
index cefdb63..4a65a01 100644
--- a/src/skillmodels/transition_functions.py
+++ b/src/skillmodels/transition_functions.py
@@ -47,6 +47,22 @@ def params_linear(factors):
     return [*factors, "constant"]
 
 
+def identity_constraints_linear(factor, period, all_factors) -> list[dict]:
+    """Index tuples for linear transition function."""
+    constraints_dicts = []
+    for regressor in params_linear(all_factors):
+        val = 1.0 if factor == regressor else 0.0
+        constraints_dicts.append(
+            {
+                "loc": ("transition", period, factor, regressor),
+                "type": "fixed",
+                "value": val,
+                "description": "Identity constraint.",
+            }
+        )
+    return constraints_dicts
+
+
 def translog(states, params):
     """Translog transition function.
 
@@ -80,6 +96,22 @@ def params_translog(factors):
     return names
 
 
+def identity_constraints_translog(factor, period, all_factors) -> list[dict]:
+    """Index tuples for translog transition function."""
+    constraints_dicts = []
+    for regressor in params_translog(all_factors):
+        val = 1.0 if factor == regressor else 0.0
+        constraints_dicts.append(
+            {
+                "loc": ("transition", period, factor, regressor),
+                "type": "fixed",
+                "value": val,
+                "description": "Identity constraint.",
+            }
+        )
+    return constraints_dicts
+
+
 def log_ces(states, params):
     """Log CES production function (KLS version)."""
     phi = params[-1]
@@ -107,6 +139,10 @@ def constraints_log_ces(factor, factors, period):
     return {"loc": loc, "type": "probability"}
 
 
+def identity_constraints_log_ces(factors, period, all_factors):
+    raise NotImplementedError
+
+
 def constant(state, params):  # noqa: ARG001
     """Constant production function."""
     return state
@@ -137,6 +173,10 @@ def params_robust_translog(factors):
     return params_translog(factors)
 
 
+def identity_constraints_robust_translog(factor, period, all_factors) -> list[dict]:
+    return identity_constraints_translog(factor, period, all_factors)
+
+
 def linear_and_squares(states, params):
     """linear_and_squares transition function."""
     nfac = len(states)
@@ -156,6 +196,22 @@ def params_linear_and_squares(factors):
     return names
 
 
+def identity_constraints_linear_and_squares(factor, period, all_factors) -> list[dict]:
+    """Index tuples for linear_and_squares transition function."""
+    constraints_dicts = []
+    for regressor in params_linear_and_squares(all_factors):
+        val = 1.0 if factor == regressor else 0.0
+        constraints_dicts.append(
+            {
+                "loc": ("transition", period, factor, regressor),
+                "type": "fixed",
+                "value": val,
+                "description": "Identity constraint.",
+            }
+        )
+    return constraints_dicts
+
+
 def log_ces_general(states, params):
     """Generalized log_ces production function without known location and scale."""
     n = states.shape[-1]
@@ -176,3 +232,7 @@ def log_ces_general(states, params):
 def params_log_ces_general(factors):
     """Index tuples for the generalized log_ces production function."""
     return factors + [f"sigma_{fac}" for fac in factors] + ["tfp"]
+
+
+def identity_constraints_log_ces_general(factors, period, all_factors):
+    raise NotImplementedError
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 536f39a..8cff204 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -376,7 +376,7 @@ def _get_states_data(model, period, data, states, observed_factors):
     if observed_factors:
         _observed_arr = process_data(
             df=data,
-            has_investments=model["has_investments"],
+            has_investments=model["investments_info"]["has_investments"],
             labels=model["labels"],
             update_info=model["update_info"],
             anchoring_info=model["anchoring"],
diff --git a/tests/simplest_augmented_model.yaml b/tests/simplest_augmented_model.yaml
index 9b6b442..b2d2b52 100644
--- a/tests/simplest_augmented_model.yaml
+++ b/tests/simplest_augmented_model.yaml
@@ -6,6 +6,10 @@ factors:
     measurements:
       -   - var
       -   - var
+    normalizations:
+      loadings:
+        - {var: 1}
+        - {var: 1}
     transition_function: linear
   fac2:
     is_correction: false
@@ -13,6 +17,10 @@ factors:
     measurements:
       -   - inv
       -   - inv
+    normalizations:
+      loadings:
+        - {inv: 1}
+        - {inv: 1}
     transition_function: linear
 observed_factors:
   - of
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index 9328624..040121f 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -1,11 +1,15 @@
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
 import pytest
+import yaml
 from pandas.testing import assert_frame_equal
 
 from skillmodels.constraints import (
     _get_anchoring_constraints,
     _get_constant_factors_constraints,
+    _get_constraints_for_augmented_periods,
     _get_initial_states_constraints,
     _get_mixture_weights_constraints,
     _get_normalization_constraints,
@@ -13,6 +17,10 @@
     _get_transition_constraints,
     add_bounds,
 )
+from skillmodels.process_model import process_model
+
+# importing the TEST_DIR from config does not work for test run in conda build
+TEST_DIR = Path(__file__).parent.resolve()
 
 
 def test_add_bounds():
@@ -124,7 +132,35 @@ def test_stage_constraints():
         },
     ]
 
-    calculated = _get_stage_constraints(stagemap, stages)
+    calculated = _get_stage_constraints(stagemap=stagemap, stages=stages)
+    for c in calculated:
+        del c["description"]
+    assert_list_equal_except_for_order(calculated, expected)
+
+
+def test_stage_constraints_with_investments():
+    stages = [0, 1, 2, 3]
+    stagemap = [0, 1, 0, 1, 2, 3]
+    expected = [
+        {
+            "locs": [("transition", 0), ("transition", 2)],
+            "type": "pairwise_equality",
+        },
+        {
+            "locs": [("transition", 1), ("transition", 3)],
+            "type": "pairwise_equality",
+        },
+        {
+            "locs": [("shock_sds", 0), ("shock_sds", 2)],
+            "type": "pairwise_equality",
+        },
+        {
+            "locs": [("shock_sds", 1), ("shock_sds", 3)],
+            "type": "pairwise_equality",
+        },
+    ]
+
+    calculated = _get_stage_constraints(stagemap=stagemap, stages=stages)
     for c in calculated:
         del c["description"]
     assert_list_equal_except_for_order(calculated, expected)
@@ -326,11 +362,47 @@ def test_anchoring_constraints_for_loadings(anch_uinfo, base_anchoring_info):
     assert calculated == expected
 
 
-# ======================================================================================
-
-
 def assert_list_equal_except_for_order(list1, list2):
     for item in list1:
         assert item in list2, f"{item} is in list1 but not in list2"
     for item in list2:
         assert item in list1, f"{item} is in list2 but not in list1"
+
+
+@pytest.fixture
+def simplest_augmented_model():
+    with open(TEST_DIR / "simplest_augmented_model.yaml") as y:
+        model_dict = yaml.load(y, Loader=yaml.FullLoader)
+    return process_model(model_dict)
+
+
+def test_get_constraints_for_augmented_periods(simplest_augmented_model):
+    calculated = _get_constraints_for_augmented_periods(
+        labels=simplest_augmented_model["labels"],
+        investments_info=simplest_augmented_model["investments_info"],
+    )
+    for c in calculated:
+        del c["description"]
+    expected = [
+        {"loc": ("transition", 1, "fac1", "fac1"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 1, "fac1", "fac2"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 1, "fac1", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 1, "fac1", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 1, "fac1", "-"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 3, "fac1", "fac1"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 3, "fac1", "fac2"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 3, "fac1", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 3, "fac1", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 3, "fac1", "-"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 0, "fac2", "fac1"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 0, "fac2", "fac2"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 0, "fac2", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 0, "fac2", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 0, "fac2", "-"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 2, "fac2", "fac1"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 2, "fac2", "fac2"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 2, "fac2", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 2, "fac2", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 2, "fac2", "-"), "type": "fixed", "value": 0.0},
+    ]
+    assert_list_equal_except_for_order(calculated, expected)
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index b8cacb9..aed0228 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -24,7 +24,7 @@ def model2():
 
 
 def test_has_investments(model2):
-    assert process_model(model2)["has_investments"] == False
+    assert process_model(model2)["investments_info"]["has_investments"] == False
 
 
 def test_dimensions(model2):
@@ -138,18 +138,30 @@ def test_anchoring_and_investments_conflict():
         process_model(model_dict)
 
 
-def test_stages_and_investments_conflict():
+def test_stagemap_with_investments_wrong_labels():
     with open(TEST_DIR / "model2.yaml") as y:
         model_dict = yaml.load(y, Loader=yaml.FullLoader)
     # Set fac3 to be an investment
     model_dict["factors"]["fac3"]["is_investment"] = True
+    model_dict["stagemap"] = [0, 0, 1, 1, 2, 2, 4]
     del model_dict["anchoring"]
-    with pytest.raises(
-        ValueError, match="Stages currently not supported when investments are present"
-    ):
+    with pytest.raises(ValueError, match="Invalid stage map:"):
         process_model(model_dict)
 
 
+def test_stagemap_with_investments():
+    with open(TEST_DIR / "model2.yaml") as y:
+        model_dict = yaml.load(y, Loader=yaml.FullLoader)
+    # Set fac3 to be an investment
+    model_dict["factors"]["fac3"]["is_investment"] = True
+    model_dict["stagemap"] = [0, 0, 1, 1, 2, 2, 3]
+    del model_dict["anchoring"]
+    model = process_model(model_dict)
+    assert model["labels"]["stagemap_raw"] == model_dict["stagemap"]
+    assert model["labels"]["stages_raw"] == [0, 1, 2, 3]
+    assert model["labels"]["stagemap"] == [0, 1, 0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7]
+
+
 @pytest.fixture
 def model2_inv():
     with open(TEST_DIR / "model2.yaml") as y:
@@ -162,7 +174,7 @@ def model2_inv():
 
 
 def test_with_inv_has_investments(model2_inv):
-    assert process_model(model2_inv)["has_investments"] == True
+    assert process_model(model2_inv)["investments_info"]["has_investments"] == True
 
 
 def test_with_inv_dimensions(model2_inv):
@@ -178,14 +190,15 @@ def test_with_inv_dimensions(model2_inv):
 
 def test_with_inv_labels(model2_inv):
     res = process_model(model2_inv)["labels"]
+    n_periods = 16
     assert res["latent_factors"] == ["fac1", "fac2", "fac3"]
     assert res["observed_factors"] == []
     assert res["all_factors"] == ["fac1", "fac2", "fac3"]
     assert res["controls"] == ["constant", "x1"]
-    assert res["periods"] == list(range(16))
+    assert res["periods"] == list(range(n_periods))
     assert res["periods_raw"] == [0, 1, 2, 3, 4, 5, 6, 7]
-    assert res["stagemap"] == list(range(15))
-    assert res["stages"] == list(range(15))
+    assert res["stagemap"] == list(range(n_periods - 2))
+    assert res["stages"] == list(range(n_periods - 2))
 
 
 def test_with_inv_estimation_options(model2_inv):

From cd2e337a452eec619844fc1d0a610ade2f4e4e21 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Thu, 27 Feb 2025 15:30:44 +0100
Subject: [PATCH 18/48] Update pixi on GHA.

---
 .github/workflows/main.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 99126fd..0d90098 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -23,12 +23,12 @@ jobs:
           - macos-latest
           - windows-latest
         python-version:
-          - '3.12'
+          - '3.13'
     steps:
       - uses: actions/checkout@v4
       - uses: prefix-dev/setup-pixi@v0.8.0
         with:
-          pixi-version: v0.29.0
+          pixi-version: v0.41.4
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: test-cpu
@@ -37,12 +37,12 @@ jobs:
         shell: bash -l {0}
         run: pixi run -e test-cpu tests-with-cov
       - name: Upload coverage report
-        if: runner.os == 'Linux' && matrix.python-version == '3.12'
+        if: runner.os == 'Linux' && matrix.python-version == '3.13'
         uses: codecov/codecov-action@v4
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
   # run-mypy:
-  #   name: Run mypy on Python 3.12
+  #   name: Run mypy on Python 3.13
   #   runs-on: ubuntu-latest
   #   strategy:
   #     fail-fast: false
@@ -58,7 +58,7 @@ jobs:
   #       shell: bash -l {0}
   #       run: pixi run mypy
   # run-explanation-notebooks:
-  #   name: Run explanation notebooks on Python 3.12
+  #   name: Run explanation notebooks on Python 3.13
   #   runs-on: ubuntu-latest
   #   steps:
   #     - uses: actions/checkout@v4

From 553b4fd21b6767ad06455382077c37749d0ad0ec Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Thu, 27 Feb 2025 15:37:04 +0100
Subject: [PATCH 19/48] Fix requirements for MacOS.

---
 pixi.lock      | 2015 +++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |   16 +-
 2 files changed, 2018 insertions(+), 13 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 1eb2346..e10c206 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -519,6 +519,215 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/29/93/d56fb9ba5569dc29d8263c72e46d21a2fd38741339ebf03f54cf7561828c/pdbp-1.6.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/65/44/bb509c3d2c0b5a87e7a5af1d5917a402a32ff026f777a6d7cb6990746cbb/tabcompleter-1.4.0-py3-none-any.whl
       - pypi: .
+      osx-arm64:
+      - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.8.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/argon2-cffi-bindings-21.2.0-py313h20a7fcf_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.4-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.1.0-pyh71513ae_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.3-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.2.0-pyh29332c3_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.2.0-h82add2a_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-1.1.0-hd74edd7_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-hd74edd7_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py313h3579c5c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2025.1.31-hf0a4a13_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.1.31-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.17.1-py313hc845a76_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.1-py313h0ebd0e5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.12-py313h928ef07_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.9-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/filterpy-1.4.5-pyhd8ed1ab_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.56.0-py313ha9b7d5b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.12.1-hadb7bae_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/greenlet-3.1.1-py313h928ef07_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.14.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.8-pyhd8ed1ab_0.conda
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+  - libbrotlienc 1.1.0 hd74edd7_2
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+  license: MIT
+  license_family: MIT
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+  - libbrotlienc 1.1.0 hd74edd7_2
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@@ -2419,6 +3128,25 @@ packages:
   - pkg:pypi/brotli?source=hash-mapping
   size: 350424
   timestamp: 1725267803672
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py313h3579c5c_2.conda
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+  - python_abi 3.13.* *_cp313
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.17.1-py313hc845a76_0.conda
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+  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.1-py313h0ebd0e5_0.conda
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+  - python_abi 3.13.* *_cp313
+  arch: arm64
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+  license: BSD-3-Clause
+  license_family: BSD
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.6.12-py313ha9b7d5b_0.conda
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+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  - tomli
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.12-py313h928ef07_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.56.0-py313ha9b7d5b_0.conda
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+  - python_abi 3.13.* *_cp313
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/greenlet-3.1.1-py313h928ef07_1.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/jsonpointer-3.0.0-py313h8f79df9_1.conda
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   md5: 4abd500577430a942a995fd0d09b76a2
@@ -7981,6 +9839,26 @@ packages:
   - pkg:pypi/textual?source=hash-mapping
   size: 470602
   timestamp: 1740299880441
+- conda: https://conda.anaconda.org/conda-forge/noarch/textual-2.1.2-pyhd8ed1ab_0.conda
+  sha256: a41f7379558f38a8a648697a3d73ff72206e4daed4f8eb8b879264c7f7c19892
+  md5: 7485a9a570ebad99950ec6e03ccea235
+  depends:
+  - linkify-it-py >=1,<3
+  - markdown-it-py >=2.1.0
+  - mdit-py-plugins
+  - platformdirs >=3.6.0,<5
+  - python >=3.9,<4.0.0
+  - rich >=13.3.3
+  - typing-extensions >=4.4.0,<5.0.0
+  - typing_extensions >=4.4.0,<5.0.0
+  constrains:
+  - tree_sitter >=0.23.0
+  - tree_sitter_languages =1.10.2
+  license: MIT
+  purls:
+  - pkg:pypi/textual?source=hash-mapping
+  size: 470192
+  timestamp: 1740642969385
 - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda
   sha256: cad582d6f978276522f84bd209a5ddac824742fe2d452af6acf900f8650a73a2
   md5: f1acf5fdefa8300de697982bcb1761c9
@@ -8004,6 +9882,18 @@ packages:
   purls: []
   size: 3318875
   timestamp: 1699202167581
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h5083fa2_1.conda
+  sha256: 72457ad031b4c048e5891f3f6cb27a53cb479db68a52d965f796910e71a403a8
+  md5: b50a57ba89c32b62428b71a875291c9b
+  depends:
+  - libzlib >=1.2.13,<2.0.0a0
+  arch: arm64
+  platform: osx
+  license: TCL
+  license_family: BSD
+  purls: []
+  size: 3145523
+  timestamp: 1699202432999
 - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda
   sha256: 2c4e914f521ccb2718946645108c9bd3fc3216ba69aea20c2c3cedbd8db32bb1
   md5: fc048363eb8f03cd1737600a5d08aafe
@@ -8052,6 +9942,22 @@ packages:
   - pkg:pypi/tornado?source=hash-mapping
   size: 861808
   timestamp: 1732615990936
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.4.2-py313h90d716c_0.conda
+  sha256: 33ef243265af82d7763c248fedd9196523210cc295b2caa512128202eda5e9e8
+  md5: 6790d50f184874a9ea298be6bcbc7710
+  depends:
+  - __osx >=11.0
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  arch: arm64
+  platform: osx
+  license: Apache-2.0
+  license_family: Apache
+  purls:
+  - pkg:pypi/tornado?source=hash-mapping
+  size: 863363
+  timestamp: 1732616174714
 - conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.4.2-py313ha7868ed_0.conda
   sha256: 062e8b77b825463fc59f373d4033fae7cf65a4170e761814bcbf25cd0627bd1d
   md5: 3d63fe6a4757924a085ab10196049854
@@ -8174,6 +10080,24 @@ packages:
   - pkg:pypi/ukkonen?source=hash-mapping
   size: 13916
   timestamp: 1725784177558
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ukkonen-1.0.1-py313hf9c7212_5.conda
+  sha256: 482eac475928c031948790647ae10c2cb1d4a779c2e8f35f5fd1925561b13203
+  md5: 8ddba23e26957f0afe5fc9236c73124a
+  depends:
+  - __osx >=11.0
+  - cffi
+  - libcxx >=17
+  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  arch: arm64
+  platform: osx
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/ukkonen?source=hash-mapping
+  size: 13689
+  timestamp: 1725784235751
 - conda: https://conda.anaconda.org/conda-forge/win-64/ukkonen-1.0.1-py313h1ec8472_5.conda
   sha256: 4f57f2eccd5584421f1b4d8c96c167c1008cba660d7fab5bdec1de212a0e0ff0
   md5: 97337494471e4265a203327f9a194234
@@ -8483,6 +10407,18 @@ packages:
   purls: []
   size: 14780
   timestamp: 1734229004433
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.12-h5505292_0.conda
+  sha256: f33e6f013fc36ebc200f09ddead83468544cb5c353a3b50499b07b8c34e28a8d
+  md5: 50901e0764b7701d8ed7343496f4f301
+  depends:
+  - __osx >=11.0
+  arch: arm64
+  platform: osx
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 13593
+  timestamp: 1734229104321
 - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-h0e40799_0.conda
   sha256: 047836241b2712aab1e29474a6f728647bff3ab57de2806b0bb0a6cf9a2d2634
   md5: 2ffbfae4548098297c033228256eb96e
@@ -8547,6 +10483,18 @@ packages:
   purls: []
   size: 19901
   timestamp: 1727794976192
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.5-hd74edd7_0.conda
+  sha256: 9939a166d780700d81023546759102b33fdc2c5f11ef09f5f66c77210fd334c8
+  md5: 77c447f48cab5d3a15ac224edb86a968
+  depends:
+  - __osx >=11.0
+  arch: arm64
+  platform: osx
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 18487
+  timestamp: 1727795205022
 - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.5-h0e40799_0.conda
   sha256: 9075f98dcaa8e9957e4a3d9d30db05c7578a536950a31c200854c5c34e1edb2c
   md5: 8393c0f7e7870b4eb45553326f81f0ff
@@ -8660,6 +10608,16 @@ packages:
   purls: []
   size: 89141
   timestamp: 1641346969816
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
+  sha256: 93181a04ba8cfecfdfb162fc958436d868cc37db504c58078eab4c1a3e57fbb7
+  md5: 4bb3f014845110883a3c5ee811fd84b4
+  arch: arm64
+  platform: osx
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 88016
+  timestamp: 1641347076660
 - conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h8ffe710_2.tar.bz2
   sha256: 4e2246383003acbad9682c7c63178e2e715ad0eb84f03a8df1fbfba455dfedc5
   md5: adbfb9f45d1004a26763652246a33764
@@ -8685,6 +10643,21 @@ packages:
   purls: []
   size: 335400
   timestamp: 1731585026517
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hc1bb282_7.conda
+  sha256: 9e585569fe2e7d3bea71972cd4b9f06b1a7ab8fa7c5139f92a31cbceecf25a8a
+  md5: f7e6b65943cb73bce0143737fded08f1
+  depends:
+  - __osx >=11.0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libcxx >=18
+  - libsodium >=1.0.20,<1.0.21.0a0
+  arch: arm64
+  platform: osx
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 281565
+  timestamp: 1731585108039
 - conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-ha9f60a1_7.conda
   sha256: 15cc8e2162d0a33ffeb3f7b7c7883fd830c54a4b1be6a4b8c7ee1f4fef0088fb
   md5: e03f2c245a5ee6055752465519363b1c
@@ -8727,6 +10700,25 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 424424
   timestamp: 1725305749031
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py313hf2da073_1.conda
+  sha256: 12b4e34acff24d291e2626c6610dfd819b8d99a461025ae59affcb6e84bc1d57
+  md5: deebca66926691fadaaf16da05ecb5f9
+  depends:
+  - __osx >=11.0
+  - cffi >=1.11
+  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  arch: arm64
+  platform: osx
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 336496
+  timestamp: 1725305912716
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313h574b89f_1.conda
   sha256: 1d2744ec0e91da267ce749e19142081472539cb140a7dad0646cd249246691fe
   md5: 8e017aca933f4dd25491151edd3e7820
@@ -8770,6 +10762,19 @@ packages:
   purls: []
   size: 567419
   timestamp: 1740255350233
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
+  sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09
+  md5: d96942c06c3e84bfcc5efb038724a7fd
+  depends:
+  - __osx >=11.0
+  - libzlib >=1.2.13,<2.0.0a0
+  arch: arm64
+  platform: osx
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 405089
+  timestamp: 1714723101397
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
   sha256: 768e30dc513568491818fb068ee867c57c514b553915536da09e5d10b4ebf3c3
   md5: 9a17230f95733c04dc40a2b1e5491d74
diff --git a/pyproject.toml b/pyproject.toml
index 6eb5562..ab96699 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -84,7 +84,7 @@ allow-direct-references = true
 
 [tool.pixi.project]
 channels = ["conda-forge"]
-platforms = ["linux-64", "win-64"]
+platforms = ["linux-64", "osx-arm64", "win-64"]
 
 # Development Dependencies (conda)
 # --------------------------------------------------------------------------------------
@@ -108,11 +108,11 @@ scipy = "*"
 # --------------------------------------------------------------------------------------
 
 # [tool.pixi.target.unix.dependencies]
-# jaxlib = ">=0.5"
+# jaxlib = "~=0.5"
 
 [tool.pixi.target.unix.pypi-dependencies]
-jax = ">=0.5"
-jaxlib = ">=0.5"
+jax = "~=0.5"
+jaxlib = "~=0.5"
 
 # Development Dependencies (pypi)
 # --------------------------------------------------------------------------------------
@@ -122,19 +122,19 @@ pdbp = "*"
 skillmodels = {path = ".", editable = true}
 
 [tool.pixi.target.win-64.pypi-dependencies]
-jax = { version = ">=0.5", extras = ["cpu"] }
-jaxlib = ">=0.5"
+jax = { version = "~=0.5", extras = ["cpu"] }
+jaxlib = "~=0.5"
 
 # Features and Tasks
 # --------------------------------------------------------------------------------------
 
 [tool.pixi.feature.cuda]
-platforms = ["linux-64", "osx-64", "osx-arm64"]
+platforms = ["linux-64"]
 system-requirements = {cuda = "12"}
 
 [tool.pixi.feature.cuda.target.unix.dependencies]
 cuda-nvcc = ">=12"
-# jaxlib = { version = ">=0.5", build = "cuda12*" }
+# jaxlib = { version = "~=0.5", build = "cuda12*" }
 
 [tool.pixi.feature.test.dependencies]
 pytest = "*"

From 19c8748d9f195ab7ae18634aa62d61dbc7345222 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Thu, 13 Mar 2025 16:58:23 +0100
Subject: [PATCH 20/48] Correct misnomer.

---
 src/skillmodels/maximization_inputs.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 397d308..4152ae2 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -129,7 +129,7 @@ def debug_loglike(params):
         tmp["value"] = float(tmp["value"])
         return process_debug_data(debug_data=tmp, model=model)
 
-    _constraints_tuples = get_constraints_dicts(
+    _constraints_dicts = get_constraints_dicts(
         dimensions=model["dimensions"],
         labels=model["labels"],
         anchoring_info=model["anchoring"],
@@ -138,7 +138,7 @@ def debug_loglike(params):
         investments_info=model["investments_info"],
     )
 
-    constraints = constraints_dicts_to_om(_constraints_tuples)
+    constraints = constraints_dicts_to_om(_constraints_dicts)
 
     params_template = pd.DataFrame(columns=["value"], index=p_index)
     params_template = add_bounds(
@@ -147,7 +147,7 @@ def debug_loglike(params):
     )
     params_template = enforce_fixed_constraints(
         params_template=params_template,
-        constraints_dicts=_constraints_tuples,
+        constraints_dicts=_constraints_dicts,
     )
 
     out = {

From 139212e69b33c84b493aa7a77d463a129868ac92 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Thu, 13 Mar 2025 17:01:30 +0100
Subject: [PATCH 21/48] Update & run hooks.

---
 .github/workflows/main.yml  |  8 ++++----
 .pre-commit-config.yaml     | 10 +++++-----
 src/skillmodels/__init__.py |  2 +-
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 0d90098..0e11d88 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -26,9 +26,9 @@ jobs:
           - '3.13'
     steps:
       - uses: actions/checkout@v4
-      - uses: prefix-dev/setup-pixi@v0.8.0
+      - uses: prefix-dev/setup-pixi@v0.8.3
         with:
-          pixi-version: v0.41.4
+          pixi-version: v0.42.1
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: test-cpu
@@ -48,7 +48,7 @@ jobs:
   #     fail-fast: false
   #   steps:
   #     - uses: actions/checkout@v4
-  #     - uses: prefix-dev/setup-pixi@v0.8.0
+  #     - uses: prefix-dev/setup-pixi@v0.8.3
   #       with:
   #         pixi-version: v0.28.2
   #         cache: true
@@ -62,7 +62,7 @@ jobs:
   #   runs-on: ubuntu-latest
   #   steps:
   #     - uses: actions/checkout@v4
-  #     - uses: prefix-dev/setup-pixi@v0.8.0
+  #     - uses: prefix-dev/setup-pixi@v0.8.3
   #       with:
   #         pixi-version: v0.28.2
   #         cache: true
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d3f0c8b..40d1eb6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -10,7 +10,7 @@ repos:
     hooks:
       - id: yamlfix
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: check-added-large-files
         args:
@@ -37,11 +37,11 @@ repos:
       - id: check-ast
       - id: check-docstring-first
   - repo: https://github.com/adrienverge/yamllint.git
-    rev: v1.35.1
+    rev: v1.36.0
     hooks:
       - id: yamllint
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.4
+    rev: v0.9.10
     hooks:
       # Run the linter.
       - id: ruff
@@ -59,14 +59,14 @@ repos:
           - pyi
           - jupyter
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.7.1
+    rev: 0.8.1
     hooks:
       - id: nbstripout
         args:
           - --extra-keys
           - metadata.kernelspec metadata.language_info.version metadata.vscode
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.17
+    rev: 0.7.22
     hooks:
       - id: mdformat
         additional_dependencies:
diff --git a/src/skillmodels/__init__.py b/src/skillmodels/__init__.py
index fbda207..e55229a 100644
--- a/src/skillmodels/__init__.py
+++ b/src/skillmodels/__init__.py
@@ -9,4 +9,4 @@
 from skillmodels.maximization_inputs import get_maximization_inputs
 from skillmodels.simulate_data import simulate_dataset
 
-__all__ = ["get_maximization_inputs", "simulate_dataset", "get_filtered_states"]
+__all__ = ["get_filtered_states", "get_maximization_inputs", "simulate_dataset"]

From 63b6ceb896a92d312062808d27f0c18760396136 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 17 Mar 2025 08:55:14 +0100
Subject: [PATCH 22/48] In case investments are present, leave out entire 'raw'
 period (=2 skillmodels periods) for transitions.

---
 .pre-commit-config.yaml                       |  4 +-
 src/skillmodels/filtered_states.py            | 17 ++---
 src/skillmodels/maximization_inputs.py        |  9 +--
 src/skillmodels/params_index.py               | 43 +++++++++----
 src/skillmodels/simulate_data.py              |  1 +
 src/skillmodels/utilities.py                  |  1 +
 .../visualize_transition_equations.py         |  1 +
 tests/test_params_index.py                    | 63 ++++++++++++++++++-
 8 files changed, 110 insertions(+), 29 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 40d1eb6..2b16759 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -37,11 +37,11 @@ repos:
       - id: check-ast
       - id: check-docstring-first
   - repo: https://github.com/adrienverge/yamllint.git
-    rev: v1.36.0
+    rev: v1.36.1
     hooks:
       - id: yamllint
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.10
+    rev: v0.11.0
     hooks:
       # Run the linter.
       - id: ruff
diff --git a/src/skillmodels/filtered_states.py b/src/skillmodels/filtered_states.py
index 5dd2fd7..cee2e3e 100644
--- a/src/skillmodels/filtered_states.py
+++ b/src/skillmodels/filtered_states.py
@@ -57,19 +57,20 @@ def anchor_states_df(states_df, model_dict, params):
     model = process_model(model_dict)
 
     p_index = get_params_index(
-        model["update_info"],
-        model["labels"],
-        model["dimensions"],
-        model["transition_info"],
+        update_info=model["update_info"],
+        labels=model["labels"],
+        dimensions=model["dimensions"],
+        transition_info=model["transition_info"],
+        investments_info=model["investments_info"],
     )
 
     params = params.loc[p_index]
 
     parsing_info = create_parsing_info(
-        p_index,
-        model["update_info"],
-        model["labels"],
-        model["anchoring"],
+        params_index=p_index,
+        update_info=model["update_info"],
+        labels=model["labels"],
+        anchoring=model["anchoring"],
     )
 
     *_, pardict = parse_params(
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 4152ae2..28b543c 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -56,10 +56,11 @@ def get_maximization_inputs(model_dict, data):
     """
     model = process_model(model_dict)
     p_index = get_params_index(
-        model["update_info"],
-        model["labels"],
-        model["dimensions"],
-        model["transition_info"],
+        update_info=model["update_info"],
+        labels=model["labels"],
+        dimensions=model["dimensions"],
+        transition_info=model["transition_info"],
+        investments_info=model["investments_info"],
     )
 
     parsing_info = create_parsing_info(
diff --git a/src/skillmodels/params_index.py b/src/skillmodels/params_index.py
index 4e33cbb..e6c409b 100644
--- a/src/skillmodels/params_index.py
+++ b/src/skillmodels/params_index.py
@@ -1,7 +1,9 @@
 import pandas as pd
 
 
-def get_params_index(update_info, labels, dimensions, transition_info):
+def get_params_index(
+    update_info, labels, dimensions, transition_info, investments_info
+):
     """Generate index for the params_df for optimagic.
 
     The index has four levels. The first is the parameter category. The second is the
@@ -16,25 +18,38 @@ def get_params_index(update_info, labels, dimensions, transition_info):
             factors, periods, controls, stagemap and stages. See :ref:`labels`
         options (dict): Tuning parameters for the estimation.
             See :ref:`estimation_options`.
+        transition_info (dict): Information about the transition equations.
+        investments_info (dict): Information about the investment factors, if any.
 
     Returns:
         params_index (pd.MultiIndex)
 
     """
-    ind_tups = get_control_params_index_tuples(labels["controls"], update_info)
-    ind_tups += get_loadings_index_tuples(labels["latent_factors"], update_info)
-    ind_tups += get_meas_sds_index_tuples(update_info)
-    ind_tups += get_shock_sds_index_tuples(labels["periods"], labels["latent_factors"])
+    ind_tups = get_control_params_index_tuples(
+        controls=labels["controls"], update_info=update_info
+    )
+    ind_tups += get_loadings_index_tuples(
+        factors=labels["latent_factors"], update_info=update_info
+    )
+    ind_tups += get_meas_sds_index_tuples(update_info=update_info)
+    ind_tups += get_shock_sds_index_tuples(
+        periods=labels["periods"],
+        factors=labels["latent_factors"],
+    )
     ind_tups += initial_mean_index_tuples(
-        dimensions["n_mixtures"],
-        labels["latent_factors"],
+        n_mixtures=dimensions["n_mixtures"],
+        factors=labels["latent_factors"],
     )
-    ind_tups += get_mixture_weights_index_tuples(dimensions["n_mixtures"])
+    ind_tups += get_mixture_weights_index_tuples(n_mixtures=dimensions["n_mixtures"])
     ind_tups += get_initial_cholcovs_index_tuples(
-        dimensions["n_mixtures"],
-        labels["latent_factors"],
+        n_mixtures=dimensions["n_mixtures"],
+        factors=labels["latent_factors"],
+    )
+    ind_tups += get_transition_index_tuples(
+        transition_info=transition_info,
+        periods=labels["periods"],
+        has_investments=investments_info["has_investments"],
     )
-    ind_tups += get_transition_index_tuples(transition_info, labels["periods"])
 
     index = pd.MultiIndex.from_tuples(
         ind_tups,
@@ -177,7 +192,7 @@ def get_initial_cholcovs_index_tuples(n_mixtures, factors):
     return ind_tups
 
 
-def get_transition_index_tuples(transition_info, periods):
+def get_transition_index_tuples(transition_info, periods, has_investments):
     """Index tuples for transition equation coefficients.
 
     Args:
@@ -185,14 +200,16 @@ def get_transition_index_tuples(transition_info, periods):
         all_factors (list): The latent and observed factors of the model.
         periods (list): The periods of the model
         transition_names (list): name of the transition equation of each factor
+        has_investments (bool): Whether the model has investment factors.
 
     Returns:
         ind_tups (list)
 
     """
+    end = -2 if has_investments else -1
     ind_tups = []
     for factor, names in transition_info["param_names"].items():
-        for period in periods[:-1]:
+        for period in periods[:end]:
             for name in names:
                 ind_tups.append(("transition", period, factor, name))
     return ind_tups
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index a84df7e..c56c56c 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -79,6 +79,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         labels=model["labels"],
         dimensions=model["dimensions"],
         transition_info=model["transition_info"],
+        investments_info=model["investments_info"],
     )
 
     params = params.reindex(params_index)
diff --git a/src/skillmodels/utilities.py b/src/skillmodels/utilities.py
index f0720ef..018eff6 100644
--- a/src/skillmodels/utilities.py
+++ b/src/skillmodels/utilities.py
@@ -328,6 +328,7 @@ def _get_params_index_from_model_dict(model_dict):
         labels=mod["labels"],
         dimensions=mod["dimensions"],
         transition_info=mod["transition_info"],
+        investments_info=mod["investments_info"],
     )
     return index
 
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 8cff204..8dffc3b 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -360,6 +360,7 @@ def _set_index_params(model, params):
         labels=model["labels"],
         dimensions=model["dimensions"],
         transition_info=model["transition_info"],
+        investments_info=model["investments_info"],
     )
 
     params = params.reindex(params_index)
diff --git a/tests/test_params_index.py b/tests/test_params_index.py
index e6f0bfe..ee9ffe1 100644
--- a/tests/test_params_index.py
+++ b/tests/test_params_index.py
@@ -30,6 +30,7 @@ def model2_inputs():
         "labels": processed["labels"],
         "dimensions": processed["dimensions"],
         "transition_info": processed["transition_info"],
+        "investments_info": processed["investments_info"],
     }
     return out
 
@@ -173,7 +174,7 @@ def test_initial_cov_index_tuples():
     assert calculated == expected
 
 
-def test_trans_coeffs_index_tuples():
+def test_trans_coeffs_index_tuples_no_investments():
     periods = [0, 1, 2]
 
     param_names = {
@@ -202,6 +203,64 @@ def test_trans_coeffs_index_tuples():
         ("transition", 1, "fac3", "phi"),
     ]
 
-    calculated = get_transition_index_tuples(trans_info, periods)
+    calculated = get_transition_index_tuples(
+        transition_info=trans_info,
+        periods=periods,
+        has_investments=False,
+    )
+
+    assert calculated == expected
+
+
+def test_trans_coeffs_index_tuples_has_investments():
+    periods = [0, 1, 2, 3, 4, 5]
+
+    param_names = {
+        "fac1": ["fac1", "fac2", "fac3", "constant"],
+        "fac2": [],
+        "fac3": ["fac1", "fac2", "fac3", "phi"],
+    }
+    trans_info = {"param_names": param_names}
+
+    expected = [
+        ("transition", 0, "fac1", "fac1"),
+        ("transition", 0, "fac1", "fac2"),
+        ("transition", 0, "fac1", "fac3"),
+        ("transition", 0, "fac1", "constant"),
+        ("transition", 1, "fac1", "fac1"),
+        ("transition", 1, "fac1", "fac2"),
+        ("transition", 1, "fac1", "fac3"),
+        ("transition", 1, "fac1", "constant"),
+        ("transition", 2, "fac1", "fac1"),
+        ("transition", 2, "fac1", "fac2"),
+        ("transition", 2, "fac1", "fac3"),
+        ("transition", 2, "fac1", "constant"),
+        ("transition", 3, "fac1", "fac1"),
+        ("transition", 3, "fac1", "fac2"),
+        ("transition", 3, "fac1", "fac3"),
+        ("transition", 3, "fac1", "constant"),
+        ("transition", 0, "fac3", "fac1"),
+        ("transition", 0, "fac3", "fac2"),
+        ("transition", 0, "fac3", "fac3"),
+        ("transition", 0, "fac3", "phi"),
+        ("transition", 1, "fac3", "fac1"),
+        ("transition", 1, "fac3", "fac2"),
+        ("transition", 1, "fac3", "fac3"),
+        ("transition", 1, "fac3", "phi"),
+        ("transition", 2, "fac3", "fac1"),
+        ("transition", 2, "fac3", "fac2"),
+        ("transition", 2, "fac3", "fac3"),
+        ("transition", 2, "fac3", "phi"),
+        ("transition", 3, "fac3", "fac1"),
+        ("transition", 3, "fac3", "fac2"),
+        ("transition", 3, "fac3", "fac3"),
+        ("transition", 3, "fac3", "phi"),
+    ]
+
+    calculated = get_transition_index_tuples(
+        transition_info=trans_info,
+        periods=periods,
+        has_investments=True,
+    )
 
     assert calculated == expected

From 9bc6d5972f824e189f0d965f707798f7b6f70c7c Mon Sep 17 00:00:00 2001
From: Max Jahn <max.jahn45@gmail.com>
Date: Mon, 17 Mar 2025 18:13:51 +0100
Subject: [PATCH 23/48] Fast QR Factorization on GPU (#81)

* Implement QR Factorization

* Move QR and add tests

* Fix Seed, Split tests
---
 src/skillmodels/kalman_filters.py |  7 ++-
 src/skillmodels/qr.py             | 72 +++++++++++++++++++++++++++++++
 tests/test_qr.py                  | 45 +++++++++++++++++++
 3 files changed, 123 insertions(+), 1 deletion(-)
 create mode 100644 src/skillmodels/qr.py
 create mode 100644 tests/test_qr.py

diff --git a/src/skillmodels/kalman_filters.py b/src/skillmodels/kalman_filters.py
index 37d22ef..defe35c 100644
--- a/src/skillmodels/kalman_filters.py
+++ b/src/skillmodels/kalman_filters.py
@@ -3,8 +3,13 @@
 import jax
 import jax.numpy as jnp
 
-array_qr_jax = jax.vmap(jax.vmap(jnp.linalg.qr))
+from skillmodels.qr import qr_gpu
 
+array_qr_jax = (
+    jax.vmap(jax.vmap(qr_gpu))
+    if jax.default_backend() == "gpu"
+    else jax.vmap(jax.vmap(jnp.linalg.qr))
+)
 
 # ======================================================================================
 # Update Step
diff --git a/src/skillmodels/qr.py b/src/skillmodels/qr.py
new file mode 100644
index 0000000..7e95b8a
--- /dev/null
+++ b/src/skillmodels/qr.py
@@ -0,0 +1,72 @@
+import jax
+import jax.numpy as jnp
+
+
+@jax.custom_jvp
+def qr_gpu(a: jax.Array):
+    """Custom implementation of the QR Decomposition."""
+    r, tau = jnp.linalg.qr(a, mode="raw")
+
+    q = _householder(r.mT, tau)
+    return q, jnp.triu(r.mT[: tau.shape[0]])
+
+
+def _householder(r: jax.Array, tau: jax.Array):
+    """Custom implementation of the Householder Product.
+
+    Uses the outputs of jnp.linalg.qr with mode = "raw" to calculate Q. This is needed
+    because the JAX implementation is extremely slow for a batch of small matrices.
+    """
+    m = r.shape[0]
+    n = tau.shape[0]
+    # Calculate Householder Vector which is saved in the lower triangle of R
+    v1 = jnp.expand_dims(r[:, 0], 1)
+    v1 = v1.at[0:0].set(0)
+    v1 = v1.at[0].set(1)
+    h = jnp.eye(m) - tau[0] * (v1 @ jnp.transpose(v1))
+    # Multiply all Householder Vectors Q = H(1)*H(2)...*H(n)
+    for i in range(1, n):
+        vi = jnp.expand_dims(r[:, i], 1)
+        vi = vi.at[0:i].set(0)
+        vi = vi.at[i].set(1)
+        h = h @ (jnp.eye(m) - tau[i] * (vi @ jnp.transpose(vi)))
+    return h[:, :n]
+
+
+def _t(x: jax.Array) -> jax.Array:
+    """Transpose batched Matrix."""
+    return jax.lax.transpose(x, (*range(x.ndim - 2), x.ndim - 1, x.ndim - 2))
+
+
+def _h(x: jax.Array) -> jax.Array:
+    """Hermitian Transpose of a Matrix."""
+    return _t(x).conj()
+
+
+def _tril(m: jax.Array, k: int = 0) -> jax.Array:
+    """Select lower Triangle of a Matrix."""
+    *_, dim_n, dim_m = m.shape
+    mask = jnp.tri(dim_n, dim_m, k, bool)
+    return jax.lax.select(
+        jax.lax.broadcast(mask, m.shape[:-2]), m, jax.lax.zeros_like_array(m)
+    )
+
+
+@qr_gpu.defjvp
+def qr_jvp_rule(primals, tangents):
+    """Calculates the derivative of the custom QR composition."""
+    # See j-towns.github.io/papers/qr-derivative.pdf for a terse derivation.
+    (x,) = primals
+    (dx,) = tangents
+    q, r = qr_gpu(x)
+    *_, m, n = x.shape
+    dx_rinv = jax.lax.linalg.triangular_solve(r, dx)  # Right side solve by default
+    qt_dx_rinv = _h(q) @ dx_rinv
+    qt_dx_rinv_lower = _tril(qt_dx_rinv, -1)
+    do = qt_dx_rinv_lower - _h(qt_dx_rinv_lower)  # This is skew-symmetric
+    # The following correction is necessary for complex inputs
+    i = jax.lax.expand_dims(jnp.eye(n, n), range(qt_dx_rinv.ndim - 2))
+    do = do + i * (qt_dx_rinv - qt_dx_rinv.real.astype(qt_dx_rinv.dtype))
+    dq = q @ (do - qt_dx_rinv) + dx_rinv
+    dr = (qt_dx_rinv - do) @ r
+    return (q, r), (dq, dr)
diff --git a/tests/test_qr.py b/tests/test_qr.py
new file mode 100644
index 0000000..5a35475
--- /dev/null
+++ b/tests/test_qr.py
@@ -0,0 +1,45 @@
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpy.testing import assert_array_almost_equal as aaae
+
+from skillmodels.qr import qr_gpu
+
+SEED = 20
+
+
+@pytest.fixture
+def cov_matrix():
+    fixedrng = np.random.default_rng(SEED)
+    factorized = fixedrng.uniform(low=-1, high=3, size=(7, 7))
+    cov = factorized @ factorized.T * 0.5 + np.eye(7)
+    return cov
+
+
+def test_q(cov_matrix):
+    q_gpu, _ = qr_gpu(cov_matrix)
+    q_jax, _ = jnp.linalg.qr(cov_matrix)
+    aaae(q_gpu, q_jax)
+
+
+def test_r(cov_matrix):
+    _, r_gpu = qr_gpu(cov_matrix)
+    _, r_jax = jnp.linalg.qr(cov_matrix)
+    aaae(r_gpu, r_jax)
+
+
+def test_grad_qr(cov_matrix):
+    def f_jax(a):
+        q, r = jnp.linalg.qr(a)
+        return jnp.sum(r) + jnp.sum(q)
+
+    def f_gpu(a):
+        q, r = qr_gpu(a)
+        return jnp.sum(r) + jnp.sum(q)
+
+    grad_qr_jax = jax.grad(f_jax)
+    grad_qr_gpu = jax.grad(f_gpu)
+    grad_gpu = grad_qr_gpu(cov_matrix)
+    grad_jax = grad_qr_jax(cov_matrix)
+    aaae(grad_gpu, grad_jax)

From 3a8cac46156bd658d567aaab679f310e13027878 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Mon, 17 Mar 2025 14:12:01 +0100
Subject: [PATCH 24/48] Jax via conda.

---
 pixi.lock      | 2665 ++++++++++++++++++++++--------------------------
 pyproject.toml |   26 +-
 2 files changed, 1201 insertions(+), 1490 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index e10c206..b97efbe 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -15,8 +15,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py313h536fd9c_5.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.4-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.1.0-pyh71513ae_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.5-pyh29332c3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.3.0-pyh71513ae_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.3-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_4.conda
@@ -38,39 +38,39 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py313h33d0bda_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.55-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.61-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.61-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.8.57-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.8.57-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.8.57-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-static-12.8.57-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-64-12.8.57-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-64-12.8.57-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-12.8.57-hbd13f7d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-dev-12.8.57-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-driver-dev_linux-64-12.8.57-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-12.8.61-hcdd1206_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvcc-dev_linux-64-12.8.61-he91c749_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-impl-12.8.61-h85509e4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-tools-12.8.61-he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc_linux-64-12.8.61-h04802cd_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-12.8.61-hbd13f7d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvtx-12.8.55-hbd13f7d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-12.8.61-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-12.8.61-he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.8.61-he02047a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.93-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.93-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.8.90-h5888daf_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.8.90-h5888daf_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.8.90-h3f2d84a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-static-12.8.90-h5888daf_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-64-12.8.90-h3f2d84a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-64-12.8.90-h3f2d84a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-12.8.90-hbd13f7d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-dev-12.8.90-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-driver-dev_linux-64-12.8.90-h3f2d84a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-12.8.93-hcdd1206_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvcc-dev_linux-64-12.8.93-he91c749_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-impl-12.8.93-h85509e4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-tools-12.8.93-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc_linux-64-12.8.93-h04802cd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-12.8.93-hbd13f7d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvtx-12.8.90-hbd13f7d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-12.8.93-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-12.8.93-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.8.93-he02047a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.8-h5d125a7_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cudnn-9.7.1.26-h969bcc4_0.conda
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+  - jax-rocm60-plugin>=0.5.1,<=0.5.2 ; extra == 'rocm'
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.49.1-h9eae976_1.conda
-  sha256: e4535c13b082b6126330b4b8f323d308e24f454e4f218a2a6c6135fb10050b1c
-  md5: 069213b4f57ec55bbcfaebe415c758f7
+  size: 3570805
+  timestamp: 1741857534913
+- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.49.1-h9eae976_2.conda
+  sha256: 85c5b96686a900411ad8bdd424dcc2efb2044f15488bb89e57dff3bfcb74cc65
+  md5: 1894a9d3a8c3ffa53f8ca09fcd2fac25
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - libsqlite 3.49.1 hee588c1_1
+  - libsqlite 3.49.1 hee588c1_2
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
   - readline >=8.2,<9.0a0
   license: Unlicense
   purls: []
-  size: 859431
-  timestamp: 1739953169934
+  size: 859553
+  timestamp: 1742083683966
 - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
   sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41
   md5: b1b505328da7a6b246787df4b5a49fbc
@@ -9818,27 +9621,6 @@ packages:
   - pkg:pypi/terminado?source=hash-mapping
   size: 22883
   timestamp: 1710262943966
-- conda: https://conda.anaconda.org/conda-forge/noarch/textual-2.1.1-pyhd8ed1ab_0.conda
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-  md5: b0e663e05dee5a51b4abde5aff54b76e
-  depends:
-  - linkify-it-py >=1,<3
-  - markdown-it-py >=2.1.0
-  - mdit-py-plugins
-  - platformdirs >=3.6.0,<5
-  - python >=3.9,<4.0.0
-  - rich >=13.3.3
-  - typing-extensions >=4.4.0,<5.0.0
-  - typing_extensions >=4.4.0,<5.0.0
-  constrains:
-  - tree_sitter_languages =1.10.2
-  - tree_sitter >=0.23.0
-  license: MIT
-  license_family: MIT
-  purls:
-  - pkg:pypi/textual?source=hash-mapping
-  size: 470602
-  timestamp: 1740299880441
 - conda: https://conda.anaconda.org/conda-forge/noarch/textual-2.1.2-pyhd8ed1ab_0.conda
   sha256: a41f7379558f38a8a648697a3d73ff72206e4daed4f8eb8b879264c7f7c19892
   md5: 7485a9a570ebad99950ec6e03ccea235
@@ -9855,6 +9637,7 @@ packages:
   - tree_sitter >=0.23.0
   - tree_sitter_languages =1.10.2
   license: MIT
+  license_family: MIT
   purls:
   - pkg:pypi/textual?source=hash-mapping
   size: 470192
@@ -9887,8 +9670,6 @@ packages:
   md5: b50a57ba89c32b62428b71a875291c9b
   depends:
   - libzlib >=1.2.13,<2.0.0a0
-  arch: arm64
-  platform: osx
   license: TCL
   license_family: BSD
   purls: []
@@ -9950,8 +9731,6 @@ packages:
   - python >=3.13,<3.14.0a0
   - python >=3.13,<3.14.0a0 *_cp313
   - python_abi 3.13.* *_cp313
-  arch: arm64
-  platform: osx
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -10090,8 +9869,6 @@ packages:
   - python >=3.13.0rc1,<3.14.0a0
   - python >=3.13.0rc1,<3.14.0a0 *_cp313
   - python_abi 3.13.* *_cp313
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls:
@@ -10125,19 +9902,6 @@ packages:
   - pkg:pypi/uri-template?source=hash-mapping
   size: 23990
   timestamp: 1733323714454
-- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_0.conda
-  sha256: 8cd972048f297b8e0601158ce352f5ca9510dda9f2706a46560220aa58b9f038
-  md5: 92cdb6fe54b78739ad70637e4f0deb07
-  depends:
-  - brotli-python >=1.0.9
-  - pysocks >=1.5.6,<2.0,!=1.5.7
-  - python >=3.8
-  license: MIT
-  license_family: MIT
-  purls:
-  - pkg:pypi/urllib3?source=hash-mapping
-  size: 95016
-  timestamp: 1718653149133
 - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda
   sha256: 114919ffa80c328127dab9c8e7a38f9d563c617691fb81fccb11c1e86763727e
   md5: 32674f8dbfb7b26410ed580dd3c10a29
@@ -10153,33 +9917,33 @@ packages:
   - pkg:pypi/urllib3?source=hash-mapping
   size: 100102
   timestamp: 1734859520452
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h5fd82a7_24.conda
-  sha256: 7ce178cf139ccea5079f9c353b3d8415d1d49b0a2f774662c355d3f89163d7b4
-  md5: 00cf3a61562bd53bd5ea99e6888793d0
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-hbf610ac_24.conda
+  sha256: 8ef83b62f9f0b885882d0dd41cbe47c2308f7ac0537fd508a5bbe6d3953a176e
+  md5: 9098c5cfb418fc0b0204bf2efc1e9afa
   depends:
-  - vc14_runtime >=14.40.33810
+  - vc14_runtime >=14.42.34438
   track_features:
   - vc14
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 17693
-  timestamp: 1737627189024
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34433-h6356254_24.conda
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-  md5: 2441e010ee255e6a38bf16705a756e94
+  size: 17469
+  timestamp: 1741043406253
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34438-hfd919c2_24.conda
+  sha256: fb36814355ac12dcb4a55b75b5ef0d49ec219ad9df30d7955f2ace88bd6919c4
+  md5: 5fceb7d965d59955888d9a9732719aa8
   depends:
   - ucrt >=10.0.20348.0
   constrains:
-  - vs2015_runtime 14.42.34433.* *_24
+  - vs2015_runtime 14.42.34438.* *_24
   license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
   license_family: Proprietary
   purls: []
-  size: 753531
-  timestamp: 1737627061911
-- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.29.2-pyhd8ed1ab_0.conda
-  sha256: c50a4ab0f5f1164230d42a29f12f61ece9c7b102f57ed1c607d2cd7c77e107b5
-  md5: d8a3ee355d5ecc9ee2565cafba1d3573
+  size: 751362
+  timestamp: 1741043402335
+- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.29.3-pyhd8ed1ab_0.conda
+  sha256: f7b2cd8ee05769e57dab1f2e2206360cb03d15d4290ddb30442711700c430ba6
+  md5: 87a2061465e55be9a997dd8cf8b5a578
   depends:
   - distlib >=0.3.7,<1
   - filelock >=3.12.2,<4
@@ -10189,18 +9953,18 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=hash-mapping
-  size: 3519478
-  timestamp: 1739263533376
-- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.42.34433-hfef2bbc_24.conda
-  sha256: 09102e0bd283af65772c052d85028410b0c31989b3cd96c260485d28e270836e
-  md5: 117fcc5b86c48f3b322b0722258c7259
+  size: 3520880
+  timestamp: 1741337922189
+- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.42.34438-h7142326_24.conda
+  sha256: a7104d3d605d191c8ee8d85d4175df3630d61830583494a5d1e62cd9f1260420
+  md5: 1dd2e838eb13190ae1f1e2760c036fdc
   depends:
-  - vc14_runtime >=14.42.34433
+  - vc14_runtime >=14.42.34438
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 17669
-  timestamp: 1737627066773
+  size: 17474
+  timestamp: 1741043406612
 - conda: https://conda.anaconda.org/conda-forge/linux-64/wayland-1.23.1-h3e06ad9_0.conda
   sha256: 0884b2023a32d2620192cf2e2fc6784b8d1e31cf9f137e49e00802d4daf7d1c1
   md5: 0a732427643ae5e0486a727927791da1
@@ -10371,9 +10135,9 @@ packages:
   purls: []
   size: 58628
   timestamp: 1734227592886
-- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.5-he73a12e_0.conda
-  sha256: 760f43df6c2ce8cbbbcb8f2f3b7fc0f306716c011e28d1d340f3dfa8ccf29185
-  md5: 4c3e9fab69804ec6077697922d70c6e2
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-he73a12e_0.conda
+  sha256: 277841c43a39f738927145930ff963c5ce4c4dacf66637a3d95d802a64173250
+  md5: 1c74ff8c35dcadf952a16f752ca5aa49
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -10382,11 +10146,11 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 27198
-  timestamp: 1734229639785
-- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.11-h4f16b4b_0.conda
-  sha256: a0e7fca9e341dc2455b20cd320fc1655e011f7f5f28367ecf8617cccd4bb2821
-  md5: b6eb6d0cb323179af168df8fe16fb0a1
+  size: 27590
+  timestamp: 1741896361728
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.12-h4f16b4b_0.conda
+  sha256: 51909270b1a6c5474ed3978628b341b4d4472cd22610e5f22b506855a5e20f67
+  md5: db038ce880f100acc74dba10302b5630
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -10394,8 +10158,8 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 835157
-  timestamp: 1738613163812
+  size: 835896
+  timestamp: 1741901112627
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda
   sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038
   md5: f6ebe2cb3f82ba6c057dde5d9debe4f7
@@ -10412,8 +10176,6 @@ packages:
   md5: 50901e0764b7701d8ed7343496f4f301
   depends:
   - __osx >=11.0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -10488,8 +10250,6 @@ packages:
   md5: 77c447f48cab5d3a15ac224edb86a968
   depends:
   - __osx >=11.0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -10611,8 +10371,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
   sha256: 93181a04ba8cfecfdfb162fc958436d868cc37db504c58078eab4c1a3e57fbb7
   md5: 4bb3f014845110883a3c5ee811fd84b4
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -10651,8 +10409,6 @@ packages:
   - krb5 >=1.21.3,<1.22.0a0
   - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
-  arch: arm64
-  platform: osx
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -10683,72 +10439,52 @@ packages:
   - pkg:pypi/zipp?source=hash-mapping
   size: 21809
   timestamp: 1732827613585
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py313h80202fe_1.conda
-  sha256: ea82f2b8964150a3aa7373b4697e48e64f2200fe68ae554ee85c641c692d1c97
-  md5: c178558ff516cd507763ffee230c20b2
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py313h536fd9c_1.conda
+  sha256: e884a1fc5e99904eb1c4895eb71ea7bebae35aa865422e2ff006e5b37c98d919
+  md5: 22b773d9a4bcf7a25ad5bc8591abc80f
   depends:
   - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
   - libgcc >=13
-  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 424424
-  timestamp: 1725305749031
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py313hf2da073_1.conda
-  sha256: 12b4e34acff24d291e2626c6610dfd819b8d99a461025ae59affcb6e84bc1d57
-  md5: deebca66926691fadaaf16da05ecb5f9
+  size: 737893
+  timestamp: 1741853442447
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py313h90d716c_1.conda
+  sha256: 7b5035d01ee9f5e80c7a28f198d61c818891306e3b28623a8d73eeb89e17c7ad
+  md5: fc9329ffb94f33dd18bfbaae4d9216c6
   depends:
   - __osx >=11.0
   - cffi >=1.11
-  - python >=3.13.0rc1,<3.14.0a0
-  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
   - python_abi 3.13.* *_cp313
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  arch: arm64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 336496
-  timestamp: 1725305912716
-- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313h574b89f_1.conda
-  sha256: 1d2744ec0e91da267ce749e19142081472539cb140a7dad0646cd249246691fe
-  md5: 8e017aca933f4dd25491151edd3e7820
+  size: 536091
+  timestamp: 1741853541598
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313ha7868ed_1.conda
+  sha256: 711145d9cc05efe48a093db3ceecadf18f451547c94dc15745430a39ee1556d9
+  md5: 0fe8f97370e74acbc7814c4906a5824f
   depends:
   - cffi >=1.11
-  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 325703
-  timestamp: 1725305947138
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
-  sha256: c558b9cc01d9c1444031bd1ce4b9cff86f9085765f17627a6cd85fc623c8a02b
-  md5: 4d056880988120e29d75bfff282e0f45
-  depends:
-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 554846
-  timestamp: 1714722996770
+  size: 449910
+  timestamp: 1741853538921
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_1.conda
   sha256: 532d3623961e34c53aba98db2ad0a33b7a52ff90d6960e505fb2d2efc06bb7da
   md5: 02e4e2fa41a6528afba2e54cbc4280ff
@@ -10762,32 +10498,17 @@ packages:
   purls: []
   size: 567419
   timestamp: 1740255350233
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
-  sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09
-  md5: d96942c06c3e84bfcc5efb038724a7fd
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-h6491c7d_1.conda
+  sha256: f49bbeeb3a8ead81920e6c695fff1260cbd221e2cfcdf9fb34207260fbd60816
+  md5: 66e5c4b02aa97230459efdd4f64c8ce6
   depends:
   - __osx >=11.0
-  - libzlib >=1.2.13,<2.0.0a0
-  arch: arm64
-  platform: osx
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 405089
-  timestamp: 1714723101397
-- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
-  sha256: 768e30dc513568491818fb068ee867c57c514b553915536da09e5d10b4ebf3c3
-  md5: 9a17230f95733c04dc40a2b1e5491d74
-  depends:
-  - libzlib >=1.2.13,<2.0.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 349143
-  timestamp: 1714723445995
+  size: 399981
+  timestamp: 1740255382232
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_1.conda
   sha256: a59b096b95f20910158c927797e9144ed9c7970f1b4aca58e6d6c8db9f653006
   md5: bf190adcc22f146d8ec66da215c9d78b
diff --git a/pyproject.toml b/pyproject.toml
index ab96699..41cd87e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,11 +91,11 @@ platforms = ["linux-64", "osx-arm64", "win-64"]
 
 [tool.pixi.dependencies]
 filterpy = "*"
+ipykernel = "*"
 jupyterlab = "*"
 nbformat = "*"
 networkx = "*"
 optimagic = "*"
-pixi-kernel = "*"
 plotly = ">=5.2,<6"
 pre-commit = "*"
 pybaum = "*"
@@ -103,27 +103,17 @@ python = "~=3.13"
 python-kaleido = "*"
 scipy = "*"
 
-
-# Development Dependencies (pypi)
-# --------------------------------------------------------------------------------------
-
-# [tool.pixi.target.unix.dependencies]
-# jaxlib = "~=0.5"
-
-[tool.pixi.target.unix.pypi-dependencies]
-jax = "~=0.5"
-jaxlib = "~=0.5"
-
-# Development Dependencies (pypi)
-# --------------------------------------------------------------------------------------
+[tool.pixi.target.unix.dependencies]
+jax = ">=0.5.2"
+jaxlib = ">=0.5.2"
 
 [tool.pixi.pypi-dependencies]
 pdbp = "*"
 skillmodels = {path = ".", editable = true}
 
 [tool.pixi.target.win-64.pypi-dependencies]
-jax = { version = "~=0.5", extras = ["cpu"] }
-jaxlib = "~=0.5"
+jax = { version = ">=0.5.1", extras = ["cpu"] }
+jaxlib = ">=0.5.1"
 
 # Features and Tasks
 # --------------------------------------------------------------------------------------
@@ -132,9 +122,9 @@ jaxlib = "~=0.5"
 platforms = ["linux-64"]
 system-requirements = {cuda = "12"}
 
-[tool.pixi.feature.cuda.target.unix.dependencies]
+[tool.pixi.feature.cuda.dependencies]
 cuda-nvcc = ">=12"
-# jaxlib = { version = "~=0.5", build = "cuda12*" }
+jaxlib = { version = ">=0.5.2", build = "cuda12*" }
 
 [tool.pixi.feature.test.dependencies]
 pytest = "*"

From 73fd952a76a9817521aeef755eb3caf1c87f92f4 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Fri, 21 Mar 2025 15:49:42 +0100
Subject: [PATCH 25/48] Fix (?) periods for shocks.

---
 src/skillmodels/params_index.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/skillmodels/params_index.py b/src/skillmodels/params_index.py
index e6c409b..42ccd07 100644
--- a/src/skillmodels/params_index.py
+++ b/src/skillmodels/params_index.py
@@ -35,6 +35,7 @@ def get_params_index(
     ind_tups += get_shock_sds_index_tuples(
         periods=labels["periods"],
         factors=labels["latent_factors"],
+        has_investments=investments_info["has_investments"],
     )
     ind_tups += initial_mean_index_tuples(
         n_mixtures=dimensions["n_mixtures"],
@@ -113,7 +114,7 @@ def get_meas_sds_index_tuples(update_info):
     return ind_tups
 
 
-def get_shock_sds_index_tuples(periods, factors):
+def get_shock_sds_index_tuples(periods, factors, has_investments):
     """Index tuples for shock_sd.
 
     Args:
@@ -124,8 +125,9 @@ def get_shock_sds_index_tuples(periods, factors):
         ind_tups (list)
 
     """
+    end = -2 if has_investments else -1
     ind_tups = []
-    for period in periods[:-1]:
+    for period in periods[:end]:
         for factor in factors:
             ind_tups.append(("shock_sds", period, factor, "-"))
     return ind_tups

From e1063d526e782f636bbdd4277a845d3f79adbaea Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Sat, 22 Mar 2025 08:18:10 +0100
Subject: [PATCH 26/48] Remove the last two internal periods for transitions if
 investments are present.

---
 pixi.lock                                     | 794 +++++++++---------
 src/skillmodels/filtered_states.py            |   1 +
 src/skillmodels/maximization_inputs.py        |  18 +-
 src/skillmodels/parse_params.py               |  13 +-
 src/skillmodels/simulate_data.py              |   1 +
 .../visualize_transition_equations.py         |   1 +
 tests/test_params_index.py                    |   2 +-
 tests/test_parse_params.py                    |   8 +-
 8 files changed, 441 insertions(+), 397 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index b97efbe..9ea7ca0 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10,7 +10,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.8.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_1.conda
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@@ -39,8 +39,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.93-ha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.93-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.93-ha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.93-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.8.90-h5888daf_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.8.90-h3f2d84a_1.conda
@@ -50,16 +50,16 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.8.93-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-12.8.93-ha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-12.8.93-he02047a_1.conda
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   - cuda-version >=12.8,<12.9.0a0
-  - libcusolver 11.7.3.90 h9ab20c4_0
+  - libcusolver 11.7.3.90 h9ab20c4_1
   - libgcc >=13
   - libstdcxx >=13
   constrains:
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   license: LicenseRef-NVIDIA-End-User-License-Agreement
   purls: []
-  size: 61073
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.7-ha82da77_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-20.1.1-ha82da77_0.conda
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+  md5: 833c4899914bf96caf64b52ef415e319
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   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 523505
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h4ddbbb0_0.conda
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@@ -6420,6 +6423,21 @@ packages:
   purls: []
   size: 40143643
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.1-ha7bfdaf_0.conda
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+  - libxml2 >=2.13.6,<3.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  license: Apache-2.0 WITH LLVM-exception
+  license_family: Apache
+  purls: []
+  size: 42997088
+  timestamp: 1742460259690
 - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.4-hb9d3cd8_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-12.8.93-hbd13f7d_0.conda
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-  md5: c9ef3c10605bc2bd6061301a1a46eafe
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-12.8.93-h5888daf_1.conda
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@@ -6532,8 +6550,8 @@ packages:
   - libstdcxx >=13
   license: LicenseRef-NVIDIA-End-User-License-Agreement
   purls: []
-  size: 30153882
-  timestamp: 1741364443333
+  size: 30128577
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda
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   timestamp: 1733781296133
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.7-hdb05f8b_0.conda
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-  md5: c4d54bfd3817313ce758aa76283b118d
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-20.1.1-hdb05f8b_1.conda
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+  md5: 97236e94c3a82367c5fe3a90557e6207
   depends:
   - __osx >=11.0
   constrains:
-  - openmp 19.1.7|19.1.7.*
+  - openmp 20.1.1|20.1.1.*
   license: Apache-2.0 WITH LLVM-exception
   license_family: APACHE
   purls: []
-  size: 280830
-  timestamp: 1736986295869
+  size: 282105
+  timestamp: 1742533199558
 - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda
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@@ -7440,18 +7458,19 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.1.2-pyhd8ed1ab_0.conda
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-  md5: 0982b0f06168fe3421d09f70596ca1f0
+- conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.1.3-pyh29332c3_0.conda
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+  md5: 7ec6576e328bc128f4982cd646eeba85
   depends:
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   - typing_extensions
+  - python
   license: BSD-3-Clause
   license_family: BSD
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-  size: 68903
-  timestamp: 1739952304731
+  - pkg:pypi/mistune?source=hash-mapping
+  size: 72749
+  timestamp: 1742402716323
 - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2024.2.2-h66d3029_15.conda
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@@ -7827,9 +7846,9 @@ packages:
   purls: []
   size: 2005026
   timestamp: 1738821194878
-- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.3-py313h17eae1a_0.conda
-  sha256: f9e84b3c757b57b5a875c5a96b52b9e54f184b95038b9467d995058b68896d0e
-  md5: 35e7b988e4ce49e6c402d1997c1c326f
+- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.4-py313h17eae1a_0.conda
+  sha256: d27a5b605dac225d3b9b28bd4b3dc4479210d6ae72619f56594b4d74c88cb206
+  md5: 6c905a8f170edd64f3a390c76572e331
   depends:
   - __glibc >=2.17,<3.0.a0
   - libblas >=3.9.0,<4.0a0
@@ -7845,11 +7864,11 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 8478589
-  timestamp: 1739426136517
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.2.3-py313h41a2e72_0.conda
-  sha256: a60c012a3b87e5e4cb002a41aa164b8f0e6cd8a35d63eadf0b025c259129a580
-  md5: 67d7e7f829d06baf1a53dd27b0e8b01d
+  size: 8521492
+  timestamp: 1742255362413
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.2.4-py313h41a2e72_0.conda
+  sha256: 3f4029334a82fb4f22995a0916b58a98769d00f265141f535975ec35015b9699
+  md5: 2f69d676535eff4ab82f4f8fcff974bb
   depends:
   - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
@@ -7865,11 +7884,11 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 6533531
-  timestamp: 1739426241828
-- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.3-py313hefb8edb_0.conda
-  sha256: 3a659370c9cc673afe3e6ef0e9ab90e316d33518475eb301e6347d91417ba461
-  md5: b2e5d9ca9d0a2f47f8e63cbb3c6b1e23
+  size: 6534258
+  timestamp: 1742255432786
+- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.4-py313hefb8edb_0.conda
+  sha256: 6747722f0a62af008d573c9615eadcae849ad07d936cb2d9c8cf8a2d26744098
+  md5: c724b713601d87f7157ffb495152e337
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
@@ -7884,9 +7903,9 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/numpy?source=hash-mapping
-  size: 7216883
-  timestamp: 1739426611339
+  - pkg:pypi/numpy?source=compressed-mapping
+  size: 7204910
+  timestamp: 1742255945595
 - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda
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@@ -8309,17 +8328,18 @@ packages:
   - pkg:pypi/pkgutil-resolve-name?source=hash-mapping
   size: 10693
   timestamp: 1733344619659
-- conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_1.conda
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-  md5: 577852c7e53901ddccc7e6a9959ddebe
+- conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.7-pyh29332c3_0.conda
+  sha256: ae7d3e58224d53d6b59e1f5ac5809803bb1972f0ac4fb10cd9b8c87d4122d3e0
+  md5: e57da6fe54bb3a5556cf36d199ff07d8
   depends:
   - python >=3.9
+  - python
   license: MIT
   license_family: MIT
   purls:
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-  size: 20448
-  timestamp: 1733232756001
+  - pkg:pypi/platformdirs?source=compressed-mapping
+  size: 23291
+  timestamp: 1742485085457
 - conda: https://conda.anaconda.org/conda-forge/noarch/plotly-5.24.1-pyhd8ed1ab_1.conda
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@@ -8346,9 +8366,9 @@ packages:
   - pkg:pypi/pluggy?source=hash-mapping
   size: 23595
   timestamp: 1733222855563
-- conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.1.0-pyha770c72_0.conda
-  sha256: b260b4b47956b654232f698be1b757935268830a808040aff2006d08953e9e32
-  md5: 5353f5eb201a9415b12385e35ed1148d
+- conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.2.0-pyha770c72_0.conda
+  sha256: d0bd8cce5f31ae940934feedec107480c00f67e881bf7db9d50c6fc0216a2ee0
+  md5: 17e487cc8b5507cd3abc09398cf27949
   depends:
   - cfgv >=2.0.0
   - identify >=1.0.0
@@ -8360,8 +8380,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/pre-commit?source=hash-mapping
-  size: 195101
-  timestamp: 1737408051494
+  size: 195854
+  timestamp: 1742475656293
 - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.21.1-pyhd8ed1ab_0.conda
   sha256: bc8f00d5155deb7b47702cb8370f233935704100dbc23e30747c161d1b6cf3ab
   md5: 3e01e386307acc60b2f89af0b2e161aa
@@ -9402,7 +9422,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/setuptools?source=compressed-mapping
+  - pkg:pypi/setuptools?source=hash-mapping
   size: 777736
   timestamp: 1740654030775
 - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
@@ -9418,7 +9438,7 @@ packages:
   timestamp: 1733381032766
 - pypi: .
   name: skillmodels
-  version: 0.0.53.dev11+g63b6ceb.d20250317
+  version: 0.0.53.dev14+g73fd952.d20250322
   sha256: cb2728a3f74fa0bcd2f811a631b59eca850cf046d36b8549a24a2d8a3dd8c4a2
   requires_dist:
   - dags
@@ -9773,16 +9793,16 @@ packages:
   - pkg:pypi/types-python-dateutil?source=hash-mapping
   size: 22104
   timestamp: 1733612458611
-- conda: https://conda.anaconda.org/conda-forge/noarch/types-pytz-2025.1.0.20250204-pyhd8ed1ab_0.conda
-  sha256: 66963ccd62a10772715b6e8d21ef26cd7b8bfadf3589a973c303d3dd2cfb3bdc
-  md5: 54716284517555784c24794d76a045f9
+- conda: https://conda.anaconda.org/conda-forge/noarch/types-pytz-2025.1.0.20250318-pyhd8ed1ab_0.conda
+  sha256: e461481f58b90540d4e021e1dcbfdb8f6f66c29dc554a6e49e7cb8491d7b589b
+  md5: 71c2f3c1626755e2924583af3b1e24fd
   depends:
   - python >=3.9
   license: Apache-2.0 AND MIT
   purls:
   - pkg:pypi/types-pytz?source=hash-mapping
-  size: 19063
-  timestamp: 1738736336085
+  size: 19464
+  timestamp: 1742275909245
 - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_1.conda
   noarch: python
   sha256: c8e9c1c467b5f960b627d7adc1c65fece8e929a3de89967e91ef0f726422fd32
@@ -10485,9 +10505,9 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 449910
   timestamp: 1741853538921
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_1.conda
-  sha256: 532d3623961e34c53aba98db2ad0a33b7a52ff90d6960e505fb2d2efc06bb7da
-  md5: 02e4e2fa41a6528afba2e54cbc4280ff
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
+  sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
+  md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -10496,22 +10516,22 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 567419
-  timestamp: 1740255350233
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-h6491c7d_1.conda
-  sha256: f49bbeeb3a8ead81920e6c695fff1260cbd221e2cfcdf9fb34207260fbd60816
-  md5: 66e5c4b02aa97230459efdd4f64c8ce6
+  size: 567578
+  timestamp: 1742433379869
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-h6491c7d_2.conda
+  sha256: 0d02046f57f7a1a3feae3e9d1aa2113788311f3cf37a3244c71e61a93177ba67
+  md5: e6f69c7bcccdefa417f056fa593b40f0
   depends:
   - __osx >=11.0
   - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 399981
-  timestamp: 1740255382232
-- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_1.conda
-  sha256: a59b096b95f20910158c927797e9144ed9c7970f1b4aca58e6d6c8db9f653006
-  md5: bf190adcc22f146d8ec66da215c9d78b
+  size: 399979
+  timestamp: 1742433432699
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_2.conda
+  sha256: bc64864377d809b904e877a98d0584f43836c9f2ef27d3d2a1421fa6eae7ca04
+  md5: 21f56217d6125fb30c3c3f10c786d751
   depends:
   - libzlib >=1.3.1,<2.0a0
   - ucrt >=10.0.20348.0
@@ -10520,5 +10540,5 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 353182
-  timestamp: 1740255407949
+  size: 354697
+  timestamp: 1742433568506
diff --git a/src/skillmodels/filtered_states.py b/src/skillmodels/filtered_states.py
index cee2e3e..30c542b 100644
--- a/src/skillmodels/filtered_states.py
+++ b/src/skillmodels/filtered_states.py
@@ -71,6 +71,7 @@ def anchor_states_df(states_df, model_dict, params):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
+        has_investments=model["investments_info"]["has_investments"],
     )
 
     *_, pardict = parse_params(
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 28b543c..ae4b319 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -64,10 +64,11 @@ def get_maximization_inputs(model_dict, data):
     )
 
     parsing_info = create_parsing_info(
-        p_index,
-        model["update_info"],
-        model["labels"],
-        model["anchoring"],
+        params_index=p_index,
+        update_info=model["update_info"],
+        labels=model["labels"],
+        anchoring=model["anchoring"],
+        has_investments=model["investments_info"]["has_investments"],
     )
     processed_data = process_data(
         df=data,
@@ -177,11 +178,16 @@ def _partial_some_log_likelihood(
     is_measurement_iteration = (update_info["purpose"] == "measurement").to_numpy()
     _periods = pd.Series(update_info.index.get_level_values("period").to_numpy())
     is_predict_iteration = ((_periods - _periods.shift(-1)) == -1).to_numpy()
-    last_period = model["labels"]["periods"][-1]
     # iteration_to_period is used as an indexer to loop over arrays of different lengths
     # in a jax.lax.scan. It needs to work for arrays of length n_periods and not raise
     # IndexErrors on tracer arrays of length n_periods - 1 (i.e. n_transitions).
-    # To achieve that, we replace the last period by -1.
+    # To achieve that, we replace the last period by -1. If there are investments,
+    # we replace the last two internal periods, corresponding to one raw period.
+    last_period = (
+        model["labels"]["periods"][-2]
+        if parsing_info["has_investments"]
+        else model["labels"]["periods"][-1]
+    )
     iteration_to_period = _periods.replace(last_period, -1).to_numpy()
 
     return functools.partial(
diff --git a/src/skillmodels/parse_params.py b/src/skillmodels/parse_params.py
index b7dc450..5813cee 100644
--- a/src/skillmodels/parse_params.py
+++ b/src/skillmodels/parse_params.py
@@ -5,7 +5,7 @@
 import pandas as pd
 
 
-def create_parsing_info(params_index, update_info, labels, anchoring):
+def create_parsing_info(params_index, update_info, labels, anchoring, has_investments):
     """Create a dictionary with information how the parameter vector has to be parsed.
 
     Args:
@@ -15,6 +15,8 @@ def create_parsing_info(params_index, update_info, labels, anchoring):
             in the likelihood function. See :ref:`update_info`.
         labels (dict): Dict of lists with labels for the model quantities like
             factors, periods, controls, stagemap and stages. See :ref:`labels`
+        anchoring (dict): Dictionary with anchoring settings.
+        has_investments (bool): Whether the model includes investments.
 
     Returns:
         dict: dictionary that maps model quantities to positions or slices of the
@@ -73,6 +75,9 @@ def create_parsing_info(params_index, update_info, labels, anchoring):
         "ignore_constant_when_anchoring"
     ]
 
+    # Add has_investments to parsing_info
+    parsing_info["has_investments"] = has_investments
+
     return parsing_info
 
 
@@ -207,9 +212,13 @@ def _get_transition_params(params, info, labels):
     trans_params = {}
     t_info = info["transition"]
     n_periods = len(labels["periods"])
+
+    # Use has_investments from parsing_info instead of undefined global
+    len_reduction = 2 if info["has_investments"] else 1
+
     for factor in labels["latent_factors"]:
         ilocs = t_info[factor]
-        trans_params[factor] = params[ilocs].reshape(n_periods - 1, -1)
+        trans_params[factor] = params[ilocs].reshape(n_periods - len_reduction, -1)
     return trans_params
 
 
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index c56c56c..a05e5f6 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -89,6 +89,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
+        has_investments=model["investments_info"]["has_investments"],
     )
 
     states, covs, log_weights, pardict = parse_params(
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 8dffc3b..74a6382 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -341,6 +341,7 @@ def _get_pardict(model, params):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
+        has_investments=model["investments_info"]["has_investments"],
     )
 
     _, _, _, pardict = parse_params(
diff --git a/tests/test_params_index.py b/tests/test_params_index.py
index ee9ffe1..683ada3 100644
--- a/tests/test_params_index.py
+++ b/tests/test_params_index.py
@@ -120,7 +120,7 @@ def test_shock_sd_index_tuples():
         ("shock_sds", 1, "fac2", "-"),
     ]
 
-    calculated = get_shock_sds_index_tuples(periods, factors)
+    calculated = get_shock_sds_index_tuples(periods, factors, False)
     assert calculated == expected
 
 
diff --git a/tests/test_parse_params.py b/tests/test_parse_params.py
index 5adef84..dffaf7f 100644
--- a/tests/test_parse_params.py
+++ b/tests/test_parse_params.py
@@ -38,7 +38,13 @@ def parsed_parameters():
     # more meaningful test
     anchoring = {"ignore_constant_when_anchoring": False}
 
-    parsing_info = create_parsing_info(p_index, update_info, labels, anchoring)
+    parsing_info = create_parsing_info(
+        params_index=p_index,
+        update_info=update_info,
+        labels=labels,
+        anchoring=anchoring,
+        has_investments=False,
+    )
 
     params_vec = jnp.arange(len(p_index))
     n_obs = 5

From 11a64bb54c13acf599f550da902273acbc233253 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Sat, 22 Mar 2025 08:18:38 +0100
Subject: [PATCH 27/48] Do not accidentally modify the params_template when
 enforcing constraints.

---
 src/skillmodels/constraints.py         |  3 ++-
 src/skillmodels/maximization_inputs.py |  4 ++-
 src/skillmodels/utilities.py           | 36 +++++++++++++++++++-------
 3 files changed, 31 insertions(+), 12 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index d4f8c6a..f503de1 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -496,4 +496,5 @@ def enforce_fixed_constraints(
     invalid = fixed.query("value < lower_bound or value > upper_bound")
     if len(invalid) > 0:
         raise ValueError(f"Invalid fixed constraints:\n\n{invalid}")
-    return params
+    # Setting via loc may expand the index, so reduce to the original index
+    return params.loc[params_template.index].astype(float)
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index ae4b319..1fcb557 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -151,7 +151,9 @@ def debug_loglike(params):
         params_template=params_template,
         constraints_dicts=_constraints_dicts,
     )
-
+    assert params_template.index.equals(p_index), (
+        "params_template index is not equal to p_index"
+    )
     out = {
         "loglike": loglike,
         "loglikeobs": loglikeobs,
diff --git a/src/skillmodels/utilities.py b/src/skillmodels/utilities.py
index 018eff6..154c910 100644
--- a/src/skillmodels/utilities.py
+++ b/src/skillmodels/utilities.py
@@ -97,12 +97,14 @@ def remove_factors(factors, model_dict, params=None):
         if out["anchoring"]["outcomes"] == {}:
             out = _remove_from_dict(out, "anchoring")
 
-    # Remove periods if necessary
-    new_n_periods = get_dimensions(out, has_investments)["n_periods"]
-    out = reduce_n_periods(out, new_n_periods)
+    # Remove periods if necessary, but only if no investments are present.
+    # (else we would mess up the mapping between raw periods model periods)
+    if not has_investments:
+        new_n_periods = get_dimensions(out, has_investments)["n_periods"]
+        out = reduce_n_periods(out, new_n_periods)
 
     if params is not None:
-        out_params = _reduce_params(params, out)
+        out_params = _reduce_params(params, out, has_investments)
         out = (out, out_params)
 
     return out
@@ -148,7 +150,8 @@ def remove_measurements(measurements, model_dict, params=None):
             )
 
     if params is not None:
-        out_params = _reduce_params(params, out)
+        # This likely won't work if we have investments.
+        out_params = _reduce_params(params, out, has_investments=False)
         out = (out, out_params)
 
     return out
@@ -175,7 +178,8 @@ def remove_controls(controls, model_dict, params=None):
         out = _remove_from_dict(out, "controls")
 
     if params is not None:
-        out_params = _reduce_params(params, out)
+        # This likely won't work if we have investments.
+        out_params = _reduce_params(params, out, has_investments=False)
         out = (out, out_params)
 
     return out
@@ -201,7 +205,8 @@ def switch_translog_to_linear(model_dict, params=None):
             out["factors"][factor]["transition_function"] = "linear"
 
     if params is not None:
-        out_params = _reduce_params(params, out)
+        # This likely won't work if we have investments.
+        out_params = _reduce_params(params, out, has_investments=False)
         out = (out, out_params)
 
     return out
@@ -289,7 +294,7 @@ def _remove_from_dict(dict_, to_remove):
     return {key: val for key, val in dict_.items() if key not in to_remove}
 
 
-def _reduce_params(params, model_dict):
+def _reduce_params(params, model_dict, has_investments):
     """Reduce a parameter DataFrame from a larger model to a reduced model.
 
     The reduced model must be nested in the original model for which the params
@@ -298,14 +303,25 @@ def _reduce_params(params, model_dict):
     Args:
         params (pandas.DataFrame or None): The params DataFrame for the full model.
         model_dict (dict): The model specification. See: :ref:`model_specs`.
+        has_investments (bool): Whether the model has investments.
 
     Returns:
         pandas.DataFrame: The reduced parameters DataFrame.
 
     """
     index = _get_params_index_from_model_dict(model_dict)
-    out = params.loc[index]
-    return out
+    # If we have investments, we need to keep the periods from params.
+    if has_investments:
+        df = pd.merge(
+            left=params.reset_index(),
+            right=index.to_frame(index=False)[
+                ["category", "name1", "name2"]
+            ].drop_duplicates(),
+            on=["category", "name1", "name2"],
+            how="right",
+        )
+        index = pd.MultiIndex.from_frame(df[params.index.names])
+    return params.loc[index]
 
 
 def _extend_params(params, model_dict, fill_value):

From 1589e902a59e06252b4510a63682e356fb516e18 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Tue, 25 Mar 2025 18:37:35 +0100
Subject: [PATCH 28/48] Use local optimagic.

---
 pixi.lock      | 968 ++++++++++++++++++++++++++++++-------------------
 pyproject.toml |   3 +-
 2 files changed, 598 insertions(+), 373 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 9ea7ca0..5cf9f0e 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,7 +9,6 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py313h536fd9c_5.conda
@@ -35,7 +34,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py313hfab6e84_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py313h33d0bda_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
@@ -83,7 +81,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-h48d6fc4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.2.0-hdb7739f_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.2.0-h5910c8f_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/greenlet-3.1.1-py313h46c70d0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.2.0-h2ead766_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.2.0-h9423afd_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.14.0-pyhd8ed1ab_1.conda
@@ -104,7 +101,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/jaxlib-0.5.2-cuda126py313hb1b46e1_201.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.10.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/jsonpointer-3.0.0-py313h78bf25f_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_1.conda
@@ -196,10 +192,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.1-h7b32b05_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/opt_einsum-3.4.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/optimagic-0.5.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py313ha87cce1_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda
@@ -218,16 +212,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/pybaum-0.1.3-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.2-hf636f53_101_cp313.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-2.0.7-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-kaleido-0.2.1-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.13-5_cp313.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py313h8060acc_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.3.0-py313h8e95178_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda
@@ -244,7 +237,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/sqlalchemy-2.0.39-py313h536fd9c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.49.1-h9eae976_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda
@@ -259,7 +251,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py313h33d0bda_5.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda
@@ -275,11 +267,19 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
+      - pypi: https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl
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+      - pypi: https://files.pythonhosted.org/packages/37/fa/71599c3fd06336cdc3eac52e6871cfebab4d9d70674a9a9e7a482c318e99/greenlet-3.1.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl
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+      - pypi: https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/86/2f/1f0144b14553ad32a8d0afa38b832c4b117694484c32aef2d939dc96f20a/pdbp-1.7.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/33/b7/f33743d87d0b4e7a1f12e1631a4b9a29a8d0d7c0ff9b8c896d0bf897fb60/sqlalchemy-2.0.39-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/65/44/bb509c3d2c0b5a87e7a5af1d5917a402a32ff026f777a6d7cb6990746cbb/tabcompleter-1.4.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl
       - pypi: .
+      - pypi: ../optimagic
   default:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
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+  version: '2025.2'
+  sha256: 1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8
+  requires_python: '>=2'
+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda
+  sha256: 5aaa366385d716557e365f0a4e9c3fca43ba196872abbbe3d56bb610d131e192
+  md5: 4222072737ccff51314b5ece9c7d6f5a
   license: LicenseRef-Public-Domain
   purls: []
-  size: 122921
-  timestamp: 1737119101255
+  size: 122968
+  timestamp: 1742727099393
 - conda: https://conda.anaconda.org/conda-forge/noarch/uc-micro-py-1.0.3-pyhd8ed1ab_1.conda
   sha256: a2f837780af450d633efc052219c31378bcad31356766663fb88a99e8e4c817b
   md5: 9c96c9876ba45368a03056ddd0f20431
diff --git a/pyproject.toml b/pyproject.toml
index 41cd87e..4daf70c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -95,7 +95,7 @@ ipykernel = "*"
 jupyterlab = "*"
 nbformat = "*"
 networkx = "*"
-optimagic = "*"
+# optimagic = "*"
 plotly = ">=5.2,<6"
 pre-commit = "*"
 pybaum = "*"
@@ -110,6 +110,7 @@ jaxlib = ">=0.5.2"
 [tool.pixi.pypi-dependencies]
 pdbp = "*"
 skillmodels = {path = ".", editable = true}
+optimagic = {path = "../optimagic", editable = true}
 
 [tool.pixi.target.win-64.pypi-dependencies]
 jax = { version = ">=0.5.1", extras = ["cpu"] }

From 17e5e69af1b57e5df5291d584dbf1df70b634656 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Tue, 25 Mar 2025 18:40:41 +0100
Subject: [PATCH 29/48] Clip at -1e30 in case we end up running in 32bit mode.
 Use bounds_distance instead of 0, stick to dicts for the moment though code
 for custom constraints is there.

---
 src/skillmodels/constraints.py         | 65 ++++++++++++++++++++++----
 src/skillmodels/maximization_inputs.py |  4 +-
 src/skillmodels/process_model.py       |  5 +-
 tests/simplest_augmented_model.yaml    |  3 +-
 tests/test_constraints.py              |  6 +--
 5 files changed, 67 insertions(+), 16 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index f503de1..160bf51 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -2,6 +2,7 @@
 
 import functools
 import warnings
+from dataclasses import dataclass
 from typing import Any
 
 import numpy as np
@@ -227,7 +228,7 @@ def _get_stage_constraints(stagemap, stages) -> list[dict]:
 
 
 def _get_constant_factors_constraints(labels) -> list[dict]:
-    """Fix shock variances of constant factors to 0.
+    """Fix shock variances of constant factors to `bounds_distance`.
 
     Args:
         labels (dict): Dict of lists with labels for the model quantities like
@@ -411,11 +412,12 @@ def _get_constraints_for_augmented_periods(labels, investments_info) -> list[dic
                     period=period,
                     all_factors=labels["all_factors"],
                 )
+        for period in periods_to_constrain[:-1]:
             constraints_dicts.append(
                 {
                     "loc": ("shock_sds", period, factor, "-"),
                     "type": "fixed",
-                    "value": 0.0,
+                    "value": investments_info["bounds_distance"],
                     "description": "Identity constraint.",
                 }
             )
@@ -427,6 +429,47 @@ def _sel(params, loc):
     return params.loc[loc]
 
 
+@dataclass(frozen=True)
+class SkillmodelsPairwiseEqualityConstraint(om.PairwiseEqualityConstraint):
+    loc: pd.MultiIndex | tuple | str | None = None
+    description: str | None = None
+    type: str = "Just to be able to use **constraints_dict"
+    id: int | None = None
+
+
+@dataclass(frozen=True)
+class SkillmodelsFixedConstraint(om.FixedConstraint):
+    loc: pd.MultiIndex | tuple | str | None = None
+    description: str | None = None
+    type: str = "Just to be able to use **constraints_dict"
+    id: int | None = None
+    value: float | None = None
+
+
+@dataclass(frozen=True)
+class SkillmodelsEqualityConstraint(om.EqualityConstraint):
+    loc: pd.MultiIndex | tuple | str | None = None
+    description: str | None = None
+    type: str = "Just to be able to use **constraints_dict"
+    id: int | None = None
+
+
+@dataclass(frozen=True)
+class SkillmodelsProbabilityConstraint(om.ProbabilityConstraint):
+    loc: pd.MultiIndex | tuple | str | None = None
+    description: str | None = None
+    type: str = "Just to be able to use **constraints_dict"
+    id: int | None = None
+
+
+@dataclass(frozen=True)
+class SkillmodelsIncreasingConstraint(om.IncreasingConstraint):
+    loc: pd.MultiIndex | tuple | str | None = None
+    description: str | None = None
+    type: str = "Just to be able to use **constraints_dict"
+    id: int | None = None
+
+
 def constraints_dicts_to_om(
     constraints_dicts: list[dict],
 ) -> list[om.constraints.Constraint]:
@@ -440,24 +483,26 @@ def constraints_dicts_to_om(
     Returns:
         List of optimagic constraints.
     """
+    return constraints_dicts
     om_style = []
     for c_d in constraints_dicts:
         if c_d["type"] == "pairwise_equality":
             om_style.append(
-                om.PairwiseEqualityConstraint(
-                    selectors=[functools.partial(_sel, loc=loc) for loc in c_d["locs"]]
+                SkillmodelsPairwiseEqualityConstraint(
+                    selectors=[functools.partial(_sel, loc=loc) for loc in c_d["locs"]],
+                    **c_d,
                 )
             )
         else:
             sel = functools.partial(_sel, loc=c_d["loc"])
             if c_d["type"] == "fixed":
-                om_style.append(om.FixedConstraint(selector=sel))
+                om_style.append(SkillmodelsFixedConstraint(selector=sel, **c_d))
             elif c_d["type"] == "equality":
-                om_style.append(om.EqualityConstraint(selector=sel))
+                om_style.append(SkillmodelsEqualityConstraint(selector=sel, **c_d))
             elif c_d["type"] == "probability":
-                om_style.append(om.ProbabilityConstraint(selector=sel))
+                om_style.append(SkillmodelsProbabilityConstraint(selector=sel, **c_d))
             elif c_d["type"] == "increasing":
-                om_style.append(om.IncreasingConstraint(selector=sel))
+                om_style.append(SkillmodelsIncreasingConstraint(selector=sel, **c_d))
             else:
                 raise TypeError(c_d["type"])
     return om_style
@@ -488,7 +533,9 @@ def enforce_fixed_constraints(
         for constraint in constraints_dicts:
             if constraint["type"] == "fixed":
                 params.loc[constraint["loc"], "value"] = constraint["value"]
-                params.loc[constraint["loc"], "lower_bound"] = constraint["value"]
+                params.loc[constraint["loc"], "lower_bound"] = np.nextafter(
+                    constraint["value"], -np.inf
+                )
                 params.loc[constraint["loc"], "upper_bound"] = constraint["value"]
 
     # Check that fixed constraints are valid
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 1fcb557..2e0dbfa 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -184,13 +184,15 @@ def _partial_some_log_likelihood(
     # in a jax.lax.scan. It needs to work for arrays of length n_periods and not raise
     # IndexErrors on tracer arrays of length n_periods - 1 (i.e. n_transitions).
     # To achieve that, we replace the last period by -1. If there are investments,
-    # we replace the last two internal periods, corresponding to one raw period.
+    # the last period is found at index -2 (there should not be measurements for
+    # investments in the "second half" of the last period).
     last_period = (
         model["labels"]["periods"][-2]
         if parsing_info["has_investments"]
         else model["labels"]["periods"][-1]
     )
     iteration_to_period = _periods.replace(last_period, -1).to_numpy()
+    assert max(iteration_to_period) == last_period - 1
 
     return functools.partial(
         fun,
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 25b5d88..f0f7ad9 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -56,6 +56,7 @@ def process_model(model_dict):
             has_investments=has_investments,
             model_dict=model_dict_aug,
             labels=labels,
+            bounds_distance=model_dict["estimation_options"]["bounds_distance"],
         )
     else:
         model_dict_aug = model_dict
@@ -223,7 +224,7 @@ def _process_estimation_options(model_dict):
         "sigma_points_scale": 2,
         "robust_bounds": True,
         "bounds_distance": 1e-3,
-        "clipping_lower_bound": -1e250,
+        "clipping_lower_bound": -1e30,
         "clipping_upper_bound": None,
         "clipping_lower_hardness": 1,
         "clipping_upper_hardness": 1,
@@ -398,6 +399,7 @@ def _get_investments_info(
     has_investments: bool,
     model_dict: dict[str, Any],
     labels: dict[str, Any],
+    bounds_distance: float,
 ) -> dict[str, Any]:
     """Collect information about investments."""
     investments_info = {
@@ -406,6 +408,7 @@ def _get_investments_info(
             periods=labels["periods_to_periods_raw"].keys(),
             has_investments=has_investments,
         ),
+        "bounds_distance": bounds_distance,
     }
     for fac, v in model_dict["factors"].items():
         investments_info[fac] = {
diff --git a/tests/simplest_augmented_model.yaml b/tests/simplest_augmented_model.yaml
index b2d2b52..e48aa25 100644
--- a/tests/simplest_augmented_model.yaml
+++ b/tests/simplest_augmented_model.yaml
@@ -24,4 +24,5 @@ factors:
     transition_function: linear
 observed_factors:
   - of
-estimation_options: {}
+estimation_options:
+  bounds_distance: 0.00000001
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index 040121f..9839f3b 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -388,21 +388,19 @@ def test_get_constraints_for_augmented_periods(simplest_augmented_model):
         {"loc": ("transition", 1, "fac1", "fac2"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 1, "fac1", "of"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 1, "fac1", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("shock_sds", 1, "fac1", "-"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 1, "fac1", "-"), "type": "fixed", "value": 0.00000001},
         {"loc": ("transition", 3, "fac1", "fac1"), "type": "fixed", "value": 1.0},
         {"loc": ("transition", 3, "fac1", "fac2"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 3, "fac1", "of"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 3, "fac1", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("shock_sds", 3, "fac1", "-"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 0, "fac2", "fac1"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 0, "fac2", "fac2"), "type": "fixed", "value": 1.0},
         {"loc": ("transition", 0, "fac2", "of"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 0, "fac2", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("shock_sds", 0, "fac2", "-"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 0, "fac2", "-"), "type": "fixed", "value": 0.00000001},
         {"loc": ("transition", 2, "fac2", "fac1"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 2, "fac2", "fac2"), "type": "fixed", "value": 1.0},
         {"loc": ("transition", 2, "fac2", "of"), "type": "fixed", "value": 0.0},
         {"loc": ("transition", 2, "fac2", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("shock_sds", 2, "fac2", "-"), "type": "fixed", "value": 0.0},
     ]
     assert_list_equal_except_for_order(calculated, expected)

From bbe69ceb76db1adac3759ffb86e8f2d46ffb3193 Mon Sep 17 00:00:00 2001
From: Max Jahn <max.jahn45@gmail.com>
Date: Wed, 16 Jul 2025 13:17:19 +0200
Subject: [PATCH 30/48] Reduce memory usage (#82)

* Move Checkpoints

* Add splitting for gradient

* Speed up QR
---
 src/skillmodels/kalman_filters.py      |  5 -----
 src/skillmodels/likelihood_function.py |  4 ++--
 src/skillmodels/maximization_inputs.py | 26 ++++++++++++++++++++++++--
 src/skillmodels/qr.py                  |  2 +-
 tests/test_likelihood_regression.py    | 13 +++++++++++++
 5 files changed, 40 insertions(+), 10 deletions(-)

diff --git a/src/skillmodels/kalman_filters.py b/src/skillmodels/kalman_filters.py
index defe35c..f9cfae9 100644
--- a/src/skillmodels/kalman_filters.py
+++ b/src/skillmodels/kalman_filters.py
@@ -1,5 +1,3 @@
-import functools
-
 import jax
 import jax.numpy as jnp
 
@@ -16,7 +14,6 @@
 # ======================================================================================
 
 
-@functools.partial(jax.checkpoint, prevent_cse=False)
 def kalman_update(
     states,
     upper_chols,
@@ -160,7 +157,6 @@ def calculate_sigma_scaling_factor_and_weights(n_states, kappa=2):
     return scaling_factor, weights
 
 
-@functools.partial(jax.checkpoint, static_argnums=0, prevent_cse=False)
 def kalman_predict(
     transition_func,
     states,
@@ -232,7 +228,6 @@ def kalman_predict(
     return predicted_states, predicted_covs
 
 
-@functools.partial(jax.checkpoint, prevent_cse=False)
 def _calculate_sigma_points(states, upper_chols, scaling_factor, observed_factors):
     """Calculate the array of sigma_points for the unscented transform.
 
diff --git a/src/skillmodels/likelihood_function.py b/src/skillmodels/likelihood_function.py
index 61ffee6..4e7eec2 100644
--- a/src/skillmodels/likelihood_function.py
+++ b/src/skillmodels/likelihood_function.py
@@ -132,8 +132,8 @@ def log_likelihood_obs(
         transition_func=transition_func,
         observed_factors=observed_factors,
     )
-
-    static_out = jax.lax.scan(_body, carry, loop_args)[1]
+    _body = jax.checkpoint(_body, prevent_cse=False)
+    static_out = jax.lax.scan(_body, carry, loop_args, unroll=False)[1]
 
     # clip contributions before aggregation to preserve as much information as
     # possible.
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 2e0dbfa..ae4d6cf 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -23,12 +23,14 @@
 jax.config.update("jax_enable_x64", True)  # noqa: FBT003
 
 
-def get_maximization_inputs(model_dict, data):
+def get_maximization_inputs(model_dict, data, split_dataset=1):
     """Create inputs for optimagic's maximize function.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
         data (DataFrame): dataset in long format.
+        split_dataset(Int): Controls into how many sclices to split the dataset
+            during the gradient computation.
 
     Returns a dictionary with keys:
         loglike (function): A jax jitted function that takes an optimagic-style
@@ -121,7 +123,27 @@ def loglikeobs(params):
     def loglike_and_gradient(params):
         params_vec = partialed_get_jnp_params_vec(params)
         crit = float(_jitted_loglike(params_vec))
-        grad = _to_numpy(_gradient(params_vec))
+        n_obs = processed_data["measurements"].shape[1]
+        _grad = jnp.zeros_like(params_vec)
+        start = 0
+        stop = int(n_obs / split_dataset)
+        step = int(n_obs / split_dataset)
+        for i in range(split_dataset):
+            stop = n_obs if i == split_dataset - 1 else stop
+            measurements_slice = processed_data["measurements"][:, start:stop]
+            controls_slice = processed_data["controls"][:, start:stop, :]
+            observed_factors_slice = processed_data["observed_factors"][
+                :, start:stop, :
+            ]
+            _grad += _gradient(
+                params_vec,
+                measurements=measurements_slice,
+                controls=controls_slice,
+                observed_factors=observed_factors_slice,
+            )
+            start += step
+            stop += step
+        grad = _to_numpy(_grad)
         return crit, grad
 
     def debug_loglike(params):
diff --git a/src/skillmodels/qr.py b/src/skillmodels/qr.py
index 7e95b8a..1cf835d 100644
--- a/src/skillmodels/qr.py
+++ b/src/skillmodels/qr.py
@@ -29,7 +29,7 @@ def _householder(r: jax.Array, tau: jax.Array):
         vi = jnp.expand_dims(r[:, i], 1)
         vi = vi.at[0:i].set(0)
         vi = vi.at[i].set(1)
-        h = h @ (jnp.eye(m) - tau[i] * (vi @ jnp.transpose(vi)))
+        h = h - tau[i] * (h @ vi) @ jnp.transpose(vi)
     return h[:, :n]
 
 
diff --git a/tests/test_likelihood_regression.py b/tests/test_likelihood_regression.py
index f973acf..000e4c0 100644
--- a/tests/test_likelihood_regression.py
+++ b/tests/test_likelihood_regression.py
@@ -91,6 +91,19 @@ def test_likelihood_values_have_not_changed(model2, model2_data, model_name, fun
         old_loglike = np.array(json.load(j)).sum()
     aaae(new_loglike, old_loglike)
 
+def test_splitting_does_not_change_gradient(model2, model2_data):
+
+    inputs = get_maximization_inputs(model2, model2_data)
+    inputs_split = get_maximization_inputs(model2, model2_data, 13)
+
+    params = inputs["params_template"]
+    params["value"] = 0.1
+
+    _, gradient = inputs["loglike_and_gradient"](params)
+    _, gradient_split = inputs_split["loglike_and_gradient"](params)
+
+    aaae(gradient, gradient_split)
+
 
 @pytest.mark.parametrize(
     ("model_name", "fun_key"), product(MODEL_NAMES, ["loglikeobs"])

From c65c5e44ef9d9dfac92aaf0c99a0eeb460789d01 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Wed, 16 Jul 2025 13:21:44 +0200
Subject: [PATCH 31/48] Update hooks and GHA pipeline.

---
 .github/workflows/main.yml | 4 ++--
 .pre-commit-config.yaml    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 0e11d88..97fc397 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -26,9 +26,9 @@ jobs:
           - '3.13'
     steps:
       - uses: actions/checkout@v4
-      - uses: prefix-dev/setup-pixi@v0.8.3
+      - uses: prefix-dev/setup-pixi@v0.8.10
         with:
-          pixi-version: v0.42.1
+          pixi-version: v0.49.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: test-cpu
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2b16759..e9dd8ff 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -37,11 +37,11 @@ repos:
       - id: check-ast
       - id: check-docstring-first
   - repo: https://github.com/adrienverge/yamllint.git
-    rev: v1.36.1
+    rev: v1.37.1
     hooks:
       - id: yamllint
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.0
+    rev: v0.12.3
     hooks:
       # Run the linter.
       - id: ruff

From 885ea5b0969aeba17d282993874d4ae67b91f91f Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Wed, 16 Jul 2025 13:32:43 +0200
Subject: [PATCH 32/48] Use optimagic from GitHub.

---
 pixi.lock      | 233 ++++++++++++++++++++++++++++++++-----------------
 pyproject.toml |   3 +-
 2 files changed, 157 insertions(+), 79 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 5cf9f0e..da8f9b0 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -271,15 +271,15 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f5/92/8f28e56b94ef20dcf0e04ffcfa99d5b515624f4870d420d858480171238a/dags-0.2.3-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/37/fa/71599c3fd06336cdc3eac52e6871cfebab4d9d70674a9a9e7a482c318e99/greenlet-3.1.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/e8/88/d8334e7a88a389c2031bfa0d8bac854bbd017390d76b2f4262f52bdedbb9/iminuit-2.31.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl
+      - pypi: git+https://github.com/optimagic-dev/optimagic.git?branch=main#c430ee623e7f6d229d7d4c53baa92637d8883b1c
       - pypi: https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/86/2f/1f0144b14553ad32a8d0afa38b832c4b117694484c32aef2d939dc96f20a/pdbp-1.7.0-py3-none-any.whl
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   requires_python: '>=3.10'
-  editable: true
 - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda
   sha256: 1840bd90d25d4930d60f57b4f38d4e0ae3f5b8db2819638709c36098c6ba770c
   md5: e51f1e4089cad105b6cac64bd8166587
@@ -8688,7 +8764,8 @@ packages:
   - python
   license: BSD-3-Clause
   license_family: BSD
-  purls: []
+  purls:
+  - pkg:pypi/pycparser?source=hash-mapping
   size: 110100
   timestamp: 1733195786147
 - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda
@@ -9590,10 +9667,10 @@ packages:
   - pkg:pypi/six?source=hash-mapping
   size: 16385
   timestamp: 1733381032766
-- pypi: .
+- pypi: ./
   name: skillmodels
-  version: 0.0.53.dev16+g11a64bb.d20250322
-  sha256: c83261e5f4cde072cfe1a6badeedbdf876f5e898fd6841ea07b2e1525ec03847
+  version: 0.0.53.dev20+gc65c5e4.d20250716
+  sha256: a08396c482a1ab705a886c1a1d4b60ee18982f6949fa7349395fe13ba8e3e0ae
   requires_dist:
   - dags
   - numpy
diff --git a/pyproject.toml b/pyproject.toml
index 4daf70c..507616d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -110,7 +110,8 @@ jaxlib = ">=0.5.2"
 [tool.pixi.pypi-dependencies]
 pdbp = "*"
 skillmodels = {path = ".", editable = true}
-optimagic = {path = "../optimagic", editable = true}
+# optimagic = {path = "../optimagic", editable = true}
+optimagic = { git = "https://github.com/optimagic-dev/optimagic.git", branch = "main"}
 
 [tool.pixi.target.win-64.pypi-dependencies]
 jax = { version = ">=0.5.1", extras = ["cpu"] }

From c8fb668c7e5d4ec2a6a9a8e5813d9965c1bb268e Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Sat, 4 Oct 2025 16:52:59 +0200
Subject: [PATCH 33/48] Fix heatmaps, update hooks etc.

---
 .pre-commit-config.yaml                |  6 +--
 pyproject.toml                         | 25 +++-------
 src/skillmodels/correlation_heatmap.py | 69 ++++++++++++++++----------
 src/skillmodels/simulate_data.py       |  9 +++-
 tests/test_likelihood_regression.py    |  2 +-
 5 files changed, 63 insertions(+), 48 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e9dd8ff..6364f1b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,11 +6,11 @@ repos:
       - id: check-useless-excludes
       # - id: identity  # Prints all files passed to pre-commits. Debugging.
   - repo: https://github.com/lyz-code/yamlfix
-    rev: 1.17.0
+    rev: 1.18.0
     hooks:
       - id: yamlfix
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-added-large-files
         args:
@@ -41,7 +41,7 @@ repos:
     hooks:
       - id: yamllint
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.3
+    rev: v0.13.3
     hooks:
       # Run the linter.
       - id: ruff
diff --git a/pyproject.toml b/pyproject.toml
index 507616d..024bf35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -96,26 +96,18 @@ jupyterlab = "*"
 nbformat = "*"
 networkx = "*"
 # optimagic = "*"
-plotly = ">=5.2,<6"
+plotly = ">=6.2"
 pre-commit = "*"
 pybaum = "*"
 python = "~=3.13"
-python-kaleido = "*"
+python-kaleido = ">=1.0"
 scipy = "*"
 
-[tool.pixi.target.unix.dependencies]
-jax = ">=0.5.2"
-jaxlib = ">=0.5.2"
-
 [tool.pixi.pypi-dependencies]
-pdbp = "*"
 skillmodels = {path = ".", editable = true}
-# optimagic = {path = "../optimagic", editable = true}
+jax = ">=0.7"
 optimagic = { git = "https://github.com/optimagic-dev/optimagic.git", branch = "main"}
-
-[tool.pixi.target.win-64.pypi-dependencies]
-jax = { version = ">=0.5.1", extras = ["cpu"] }
-jaxlib = ">=0.5.1"
+pdbp = "*"
 
 # Features and Tasks
 # --------------------------------------------------------------------------------------
@@ -126,7 +118,9 @@ system-requirements = {cuda = "12"}
 
 [tool.pixi.feature.cuda.dependencies]
 cuda-nvcc = ">=12"
-jaxlib = { version = ">=0.5.2", build = "cuda12*" }
+
+[tool.pixi.feature.cuda.pypi-dependencies]
+jax = {version = ">=0.7", extras = ["cuda12"]}
 
 [tool.pixi.feature.test.dependencies]
 pytest = "*"
@@ -169,7 +163,7 @@ test-gpu = {features = ["test", "cuda"], solve-group = "cuda"}
 # ======================================================================================
 
 [tool.ruff]
-target-version = "py312"
+target-version = "py313"
 fix = true
 line-length = 88
 
@@ -234,9 +228,6 @@ extend-ignore = [
     # pd.merge is fine
     "PD015",
 
-    # df is just fine
-    "PD901",
-
     # Many suggestions to use list comprehension are not helpful
     "PERF401",
 
diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index e3f9d5e..f15a620 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -134,9 +134,10 @@ def get_measurements_corr(data, model_dict, factors, periods):
             and extract measurements for each period.
         factors (list, str or NoneType): List of factors, to retrieve measurements for.
             If None, then calculate correlations of measurements of all factors.
-        periods (int,float, list or NoneType): If int, the period within which to
+        periods (int, float, list or NoneType): If int, the period within which to
             calculate measurement correlations. If a list, calculate correlations over
-            periods. If None, calculate correlations across all periods.
+            periods. If None, calculate correlations across all periods. Note: Periods
+            refer to originl periods, not the augmented periods.
 
     Returns:
         corr (DataFrame): DataFrame with measurement correlations.
@@ -145,15 +146,15 @@ def get_measurements_corr(data, model_dict, factors, periods):
     data = data.copy(deep=True)
     model = process_model(model_dict)
     periods = _process_periods(periods, model)
-    data = pre_process_data(data, periods)
+    processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = model["update_info"]
+    update_info = _get_update_info_for_periods_raw(model)
     df = _get_measurement_data(
-        data,
-        update_info,
-        periods,
-        latent_factors,
-        observed_factors,
+        data=processed_data,
+        update_info=update_info,
+        periods=periods,
+        latent_factors=latent_factors,
+        observed_factors=observed_factors,
     )
     corr = df.corr()
     return corr
@@ -186,15 +187,15 @@ def get_quasi_scores_corr(data, model_dict, factors, periods):
     data = data.copy(deep=True)
     model = process_model(model_dict)
     periods = _process_periods(periods, model)
-    data = pre_process_data(data, periods)
+    processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = model["update_info"]
+    update_info = _get_update_info_for_periods_raw(model)
     df = _get_quasi_factor_scores_data(
-        data,
-        update_info,
-        periods,
-        latent_factors,
-        observed_factors,
+        data=processed_data,
+        update_info=update_info,
+        periods=periods,
+        latent_factors=latent_factors,
+        observed_factors=observed_factors,
     )
     corr = df.corr()
     return corr
@@ -226,17 +227,17 @@ def get_scores_corr(data, params, model_dict, factors, periods):
     data = data.copy(deep=True)
     model = process_model(model_dict)
     periods = _process_periods(periods, model)
-    data = pre_process_data(data, periods)
+    processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = model["update_info"]
+    update_info = _get_update_info_for_periods_raw(model)
     params = params.loc[["controls", "loadings"]]
     df = _get_factor_scores_data(
-        data,
-        params,
-        update_info,
-        periods,
-        latent_factors,
-        observed_factors,
+        data=processed_data,
+        params=params,
+        update_info=update_info,
+        periods=periods,
+        latent_factors=latent_factors,
+        observed_factors=observed_factors,
     )
     corr = df.corr()
     return corr
@@ -273,6 +274,24 @@ def _get_mask(corr, show_upper_triangle, show_diagonal):
     return mask
 
 
+def _get_update_info_for_periods_raw(model):
+    """Transform update_info to use periods_raw instead of periods."""
+    update_info = model["update_info"].copy()
+
+    # Replace period level with period_raw using set_codes
+    period_raw_values = update_info.index.get_level_values("period").map(
+        model["labels"]["periods_to_periods_raw"]
+    )
+    update_info.index = update_info.index.set_codes(period_raw_values, level="period")
+
+    # Group by period_raw and variable, apply OR logic for boolean columns
+    cols = [col for col in update_info.columns if col != "purpose"]
+    agg_dict = dict.fromkeys(cols, "any")
+    agg_dict["purpose"] = "first"
+
+    return update_info.groupby(["period", "variable"]).agg(agg_dict)
+
+
 def _get_measurement_data(data, update_info, periods, latent_factors, observed_factors):
     """Get data frame with factor measurements in each period, in wide format.
 
@@ -708,7 +727,7 @@ def _process_factors(model, factors):
 def _process_periods(periods, model):
     """Process periods to get a list."""
     if periods is None:
-        periods = list(range(model["dimensions"]["n_periods"]))
+        periods = list(range(model["dimensions"]["n_periods_raw"]))
     elif isinstance(periods, int | float):
         periods = [periods]
     return periods
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index a05e5f6..bfdce81 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -37,7 +37,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
 
     """
     if data is None and n_obs is None:
-        raise ValueError("If data is None, n_obs has to be provided.")
+        raise ValueError("Either `data` or `n_obs` has to be provided.")
 
     model = process_model(model_dict)
 
@@ -108,6 +108,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         labels=model["labels"],
         dimensions=model["dimensions"],
         n_obs=n_obs,
+        has_investments=model["investments_info"]["has_investments"],
         update_info=model["update_info"],
         control_data=control_data,
         observed_factor_data=observed_factor_data,
@@ -150,6 +151,7 @@ def _simulate_dataset(
     labels,
     dimensions,
     n_obs,
+    has_investments,
     update_info,
     control_data,
     observed_factor_data,
@@ -168,7 +170,10 @@ def _simulate_dataset(
     policies = policies if policies is not None else []
 
     n_states = dimensions["n_latent_factors"]
-    n_periods = dimensions["n_periods"]
+    if has_investments:
+        n_periods = dimensions["n_periods"] - 1
+    else:
+        n_periods = dimensions["n_periods"]
 
     weights = np.exp(log_weights)[0]
     loadings_df = pd.DataFrame(
diff --git a/tests/test_likelihood_regression.py b/tests/test_likelihood_regression.py
index 000e4c0..cd4cbc0 100644
--- a/tests/test_likelihood_regression.py
+++ b/tests/test_likelihood_regression.py
@@ -91,8 +91,8 @@ def test_likelihood_values_have_not_changed(model2, model2_data, model_name, fun
         old_loglike = np.array(json.load(j)).sum()
     aaae(new_loglike, old_loglike)
 
-def test_splitting_does_not_change_gradient(model2, model2_data):
 
+def test_splitting_does_not_change_gradient(model2, model2_data):
     inputs = get_maximization_inputs(model2, model2_data)
     inputs_split = get_maximization_inputs(model2, model2_data, 13)
 

From e9eb49f5b55e5c5d2bb43de4f90c90af50b4db13 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Sun, 5 Oct 2025 06:11:41 +0200
Subject: [PATCH 34/48] Fix heatmaps also for factor correlations.

---
 src/skillmodels/correlation_heatmap.py | 91 ++++++++++++++++++++------
 1 file changed, 72 insertions(+), 19 deletions(-)

diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index f15a620..3dc94b3 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -229,12 +229,11 @@ def get_scores_corr(data, params, model_dict, factors, periods):
     periods = _process_periods(periods, model)
     processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = _get_update_info_for_periods_raw(model)
     params = params.loc[["controls", "loadings"]]
     df = _get_factor_scores_data(
         data=processed_data,
         params=params,
-        update_info=update_info,
+        model=model,
         periods=periods,
         latent_factors=latent_factors,
         observed_factors=observed_factors,
@@ -548,7 +547,7 @@ def _get_quasi_factor_scores_data_for_multiple_periods(
 def _get_factor_scores_data(
     data,
     params,
-    update_info,
+    model,
     periods,
     latent_factors,
     observed_factors,
@@ -563,8 +562,7 @@ def _get_factor_scores_data(
         data (pd.DataFrame): Data with observable variables.
         params (pd.DataFrame): Data frame with estimated measurement relevant
             model parameters.
-        update_info (pd.DataFrame): DataFrame with information on measurements
-            for each factor in each model period.
+        model (dict): Processed model dict.
         periods (list): The list of periods that correlations are
             calculated for.
         latent_factors (list): List of latent factors the scores of which
@@ -581,7 +579,7 @@ def _get_factor_scores_data(
         df = _get_factor_scores_data_for_single_period(
             data,
             params,
-            update_info,
+            model,
             period,
             latent_factors,
             observed_factors,
@@ -590,7 +588,7 @@ def _get_factor_scores_data(
         df = _get_factor_scores_data_for_multiple_periods(
             data,
             params,
-            update_info,
+            model,
             periods,
             latent_factors,
             observed_factors,
@@ -602,21 +600,77 @@ def _get_factor_scores_data(
 def _get_factor_scores_data_for_single_period(
     data,
     params,
-    update_info,
+    model,
     period,
     latent_factors,
     observed_factors,
 ):
     """Get frame with factor scores in a given period.
 
+    Careful: When we have investments, *period* refers to the raw period, but the
+    *params* are for the augmented periods. This function is the layer to abstract from
+    augmented periods.
+
     Args:
         data (pd.DataFrame): Data with observable variables.
         params (pd.DataFrame): Data frame with estimated measurement relevant
             model parameters.
+        model (dict): Processed model dict.
+        period (int): The period that correlations are calculated for.
+        latent_factors (list): List of latent factors the scores of which
+            correlations are calculated for.
+        observed_factors (list): List of observed factors the scores of which
+            correlations are calculated for.
+
+    Returns:
+        df (pd.DataFrame): Processed DataFrame to calculate correlations over.
+
+    """
+    model_periods = [
+        mp for mp, p in model["labels"]["periods_to_periods_raw"].items() if p == period
+    ]
+    df = pd.concat(
+        [
+            _get_factor_scores_data_for_single_model_period(
+                data=data,
+                params=params,
+                update_info=model["update_info"],
+                period=mp,
+                period_raw=period,
+                latent_factors=latent_factors,
+                observed_factors=observed_factors,
+            )
+            for mp in model_periods
+        ],
+        axis=0,
+    )
+    df = df.groupby("id").max()
+    return df.set_index(
+        pd.MultiIndex.from_tuples(
+            [(idx, period) for idx in df.index], names=["id", "period"]
+        )
+    )
+
+
+def _get_factor_scores_data_for_single_model_period(
+    data,
+    params,
+    update_info,
+    period,
+    period_raw,
+    latent_factors,
+    observed_factors,
+):
+    """Get frame with factor scores in a given model period.
+
+    In this function, all calculations are at the augmented period level.
+
+    Args:
+        data (pd.DataFrame): Data with observable variables.
+        params (pd.DataFrame): Data frame with estimated measurement relevant
         update_info (pd.DataFrame): DataFrame with information on measurements
             for each factor in each model period.
-        periods (list): The list of periods that correlations are
-            calculated for.
+        period (int): The (augmented) period that correlations are calculated for.
         latent_factors (list): List of latent factors the scores of which
             correlations are calculated for.
         observed_factors (list): List of observed factors the scores of which
@@ -624,8 +678,9 @@ def _get_factor_scores_data_for_single_period(
 
     Returns:
         df (pd.DataFrame): Processed DataFrame to calculate correlations over.
-
     """
+    if period not in update_info.index:
+        return pd.DataFrame()
     period_info = update_info.loc[period].reset_index()
     params = params.query(f"period=={period}").droplevel("period")
     loadings = params.loc["loadings"]["value"]
@@ -642,7 +697,7 @@ def _get_factor_scores_data_for_single_period(
         period_factor_measurements = [
             m for m in period_factor_measurements if m not in leave_out_meas
         ]
-        df = data.query(f"{update_info.index.names[0]}=={period}")[
+        df = data.query(f"{update_info.index.names[0]}=={period_raw}")[
             period_factor_measurements
         ]
         for m in period_factor_measurements:
@@ -651,16 +706,15 @@ def _get_factor_scores_data_for_single_period(
         sr.name = f"{factor}"
         to_concat.append(sr)
     for factor in observed_factors:
-        df = data.query(f"{update_info.index.names[0]}=={period}")[factor]
+        df = data.query(f"{update_info.index.names[0]}=={period_raw}")[factor]
         to_concat.append(df)
-    df = pd.concat(to_concat, axis=1)
-    return df
+    return pd.concat(to_concat, axis=1)
 
 
 def _get_factor_scores_data_for_multiple_periods(
     data,
     params,
-    update_info,
+    model,
     periods,
     latent_factors,
     observed_factors,
@@ -670,8 +724,7 @@ def _get_factor_scores_data_for_multiple_periods(
     Args:
         data (pd.DataFrame): Data with observable variables.
         params (pd.DataFrame): Data frame with estimated model parameters.
-        update_info (pd.DataFrame): DataFrame with information on measurements
-            for each factor in each model period.
+        model (dict): Processed model dict.
         periods (list): The list of periods that correlations are
             calculated for.
         latent_factors (list): List of latent factors the scores of which
@@ -689,7 +742,7 @@ def _get_factor_scores_data_for_multiple_periods(
             _get_factor_scores_data_for_single_period(
                 data,
                 params,
-                update_info,
+                model,
                 period,
                 latent_factors,
                 observed_factors,

From 5ddfebcdda9033dc8671f1c0f8fb447dd599ea9e Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Mon, 6 Oct 2025 08:27:40 +0200
Subject: [PATCH 35/48] Got started updating transition equations.

---
 .../visualize_transition_equations.py         | 74 +++++++++++++------
 1 file changed, 52 insertions(+), 22 deletions(-)

diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 74a6382..229510d 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -186,19 +186,19 @@ def get_transition_plots(
         "anchored_states"
     ]["states"]
     plots_dict = _get_dictionary_with_plots(
-        model,
-        data,
-        params,
-        states,
-        state_ranges,
-        latent_factors,
-        all_factors,
-        quantiles_of_other_factors,
-        period,
-        n_points,
-        n_draws,
-        colorscale,
-        layout_kwargs,
+        model=model,
+        data=data,
+        params=params,
+        states=states,
+        state_ranges=state_ranges,
+        latent_factors=latent_factors,
+        all_factors=all_factors,
+        quantiles_of_other_factors=quantiles_of_other_factors,
+        period=period,
+        n_points=n_points,
+        n_draws=n_draws,
+        colorscale=colorscale,
+        layout_kwargs=layout_kwargs,
     )
     return plots_dict
 
@@ -285,6 +285,7 @@ def _get_dictionary_with_plots(
                 input_factor=input_factor,
                 output_factor=output_factor,
                 quantiles_of_other_factors=quantiles_of_other_factors,
+                model=model,
                 n_points=n_points,
                 transition_function=transition_function,
                 transition_params=transition_params,
@@ -298,6 +299,7 @@ def _get_dictionary_with_plots(
                 period=period,
                 input_factor=input_factor,
                 output_factor=output_factor,
+                model=model,
                 n_points=n_points,
                 n_draws=n_draws,
                 transition_function=transition_function,
@@ -371,8 +373,8 @@ def _set_index_params(model, params):
 def _get_states_data(model, period, data, states, observed_factors):
     if observed_factors and data is None:
         raise ValueError(
-            """The model has observed factors. You must pass the empirical data to
-        'visualize_transition_equations' via the keyword *data*.""",
+            "The model has observed factors. You must pass the empirical data to "
+            "'visualize_transition_equations' via the keyword *data*.",
         )
 
     if observed_factors:
@@ -385,12 +387,30 @@ def _get_states_data(model, period, data, states, observed_factors):
         )["observed_factors"]
         # convert from jax to numpy
         _observed_arr = np.array(_observed_arr)
-        observed_data = pd.DataFrame(
-            data=_observed_arr[period],
-            columns=observed_factors,
-        )
-        observed_data["id"] = observed_data.index
-        observed_data["period"] = period
+        if model["investments_info"]["has_investments"]:
+            both_periods = {
+                p: period
+                for p, pr in model["labels"]["periods_to_periods_raw"].items()
+                if pr == period
+            }
+            to_concat = []
+            for p in both_periods:
+                df = pd.DataFrame(
+                    data=_observed_arr[p],
+                    columns=observed_factors,
+                )
+                df["id"] = df.index
+                df["period"] = p
+                to_concat.append(df)
+            observed_data = pd.concat(to_concat)
+        else:
+            observed_data = pd.DataFrame(
+                data=_observed_arr[period],
+                columns=observed_factors,
+            )
+            observed_data["id"] = observed_data.index
+            observed_data["period"] = period
+        # Do a left merge because we need all periods for the ranges
         states_data = pd.merge(
             left=states,
             right=observed_data,
@@ -410,6 +430,7 @@ def _prepare_data_for_one_plot_fixed_quantile_2d(
     input_factor,
     output_factor,
     quantiles_of_other_factors,
+    model,
     n_points,
     transition_function,
     transition_params,
@@ -453,13 +474,22 @@ def _prepare_data_for_one_plot_average_2d(
     period,
     input_factor,
     output_factor,
+    model,
     n_points,
     n_draws,
     transition_function,
     transition_params,
     all_factors,
 ):
-    period_data = states_data.query(f"period == {period}")[all_factors].reset_index()
+    if model["investments_info"]["has_investments"]:
+        aug_periods = [
+            p
+            for p, pr in model["labels"]["periods_to_periods_raw"].items()
+            if pr == period
+        ]
+    else:
+        aug_periods = [period]
+    period_data = states_data.query(f"period in {aug_periods}")
 
     sampled_factors = [factor for factor in all_factors if factor != input_factor]
     draws = period_data[sampled_factors].sample(n=n_draws)

From 10fc1e1ace9560caf45db5bf97cdc776131206e8 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 6 Oct 2025 11:59:45 +0200
Subject: [PATCH 36/48] Be pedantic about using 'periods' for interface and
 'aug_periods' for internal usage.

---
 pixi.lock                                     | 1549 +++++------------
 src/skillmodels/check_model.py                |   30 +-
 src/skillmodels/constraints.py                |   40 +-
 src/skillmodels/correlation_heatmap.py        |   24 +-
 src/skillmodels/maximization_inputs.py        |   18 +-
 src/skillmodels/params_index.py               |    4 +-
 src/skillmodels/parse_params.py               |    4 +-
 src/skillmodels/process_data.py               |   62 +-
 src/skillmodels/process_model.py              |  152 +-
 src/skillmodels/simulate_data.py              |    4 +-
 .../visualize_transition_equations.py         |   22 +-
 tests/simplest_augmented_data_expected.csv    |   26 +-
 tests/test_constraints.py                     |    4 +-
 tests/test_process_data.py                    |    8 +-
 tests/test_process_model.py                   |   22 +-
 15 files changed, 699 insertions(+), 1270 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index da8f9b0..582516d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -26,7 +26,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py313h46c70d0_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
@@ -34,6 +33,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py313hfab6e84_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/choreographer-1.1.1-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py313h33d0bda_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
@@ -45,21 +45,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-static-12.8.90-h5888daf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-64-12.8.90-h3f2d84a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-64-12.8.90-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-12.8.90-hbd13f7d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-dev-12.8.90-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-driver-dev_linux-64-12.8.90-h3f2d84a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-12.8.93-hcdd1206_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvcc-dev_linux-64-12.8.93-he91c749_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-impl-12.8.93-h85509e4_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-tools-12.8.93-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc_linux-64-12.8.93-h04802cd_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-12.8.93-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvtx-12.8.90-hbd13f7d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-12.8.93-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-12.8.93-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.8.93-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.8-h5d125a7_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cudnn-9.8.0.87-h81d5506_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.13-py313h46c70d0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda
@@ -67,15 +62,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.9-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.18.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filterpy-1.4.5-pyhd8ed1ab_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.56.0-py313h8060acc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-h48d6fc4_0.conda
@@ -97,8 +85,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.0.2-pyhfb0248b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jax-0.5.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/jaxlib-0.5.2-cuda126py313hb1b46e1_201.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.10.0-pyhd8ed1ab_1.conda
@@ -115,7 +101,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.3.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.27.3-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/kaleido-core-0.2.1-h3644ca4_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-3.10.0-he073ed8_18.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.7-py313h33d0bda_0.conda
@@ -123,22 +108,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250127.1-cxx17_hbbce691_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcublas-12.8.4.1-h9ab20c4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcublas-dev-12.8.4.1-h9ab20c4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufft-11.3.3.83-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufft-dev-11.3.3.83-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurand-10.3.9.90-h9ab20c4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurand-dev-10.3.9.90-h9ab20c4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-11.7.3.90-h9ab20c4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-dev-11.7.3.90-h9ab20c4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-12.5.8.93-hbd13f7d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-dev-12.5.8.93-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h4ddbbb0_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda
@@ -149,16 +123,12 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-12.8.93-h5888daf_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/logistro-1.1.0-pyhe01879c_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/opt_einsum-3.4.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.11.3-py313h843e2db_1.conda
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@@ -201,7 +168,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/plotly-5.24.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/plotly-6.3.1-pyhd8ed1ab_0.conda
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@@ -218,13 +185,12 @@ environments:
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-- conda: https://conda.anaconda.org/conda-forge/noarch/python-kaleido-0.2.1-pyhd8ed1ab_0.tar.bz2
-  sha256: e17bf63a30aec33432f1ead86e15e9febde9fc40a7f869c0e766be8d2db44170
-  md5: 310259a5b03ff02289d7705f39e2b1d2
+- conda: https://conda.anaconda.org/conda-forge/noarch/python-kaleido-1.0.0-pyhe01879c_1.conda
+  sha256: f57d5f878e7630f6f29df43d15863ff9cb79123e85b970760cd78a08fda2152f
+  md5: 4106d29b2e11b71fc25c2e2388d1b354
   depends:
-  - kaleido-core 0.2.1.*
-  - python >=3.5
+  - python >=3.9
+  - choreographer >=1.0.5
+  - logistro >=1.0.8
+  - orjson >=3.10.15
+  - packaging
+  - python
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/kaleido?source=hash-mapping
-  size: 18320
-  timestamp: 1615204747600
+  size: 50867
+  timestamp: 1755190006493
 - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.13-5_cp313.conda
   build_number: 5
   sha256: 438225b241c5f9bddae6f0178a97f5870a89ecf927dfca54753e689907331442
@@ -9380,26 +8797,6 @@ packages:
   purls: []
   size: 94138960
   timestamp: 1738337004104
-- conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h9925aae_3.conda
-  sha256: 66d34e3b4881f856486d11914392c585713100ca547ccfc0947f3a4765c2c486
-  md5: 6f445fb139c356f903746b2b91bbe786
-  depends:
-  - libre2-11 2024.07.02 hba17884_3
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 26811
-  timestamp: 1741121137599
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2024.07.02-h6589ca4_3.conda
-  sha256: 248af2869bf54f77f5b4c6e144b535bbc2a6d4c27228f4fb2ed689f8df9f071b
-  md5: d4e82bd66b71c29da35e1f634548e039
-  depends:
-  - libre2-11 2024.07.02 hd41c47c_3
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 26954
-  timestamp: 1741121389739
 - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda
   sha256: 2d6d0c026902561ed77cd646b5021aef2d4db22e57a5b0178dfc669231e06d2c
   md5: 283b96675859b20a825f8fa30f311446
@@ -9656,6 +9053,49 @@ packages:
   - pkg:pypi/setuptools?source=hash-mapping
   size: 777736
   timestamp: 1740654030775
+- conda: https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.20.2-py313h07c4f96_0.conda
+  sha256: 90980dba720b342b017b20b74cd5248e9553cb16751a9753fed1f6c01128ab8e
+  md5: 27c2dcd271584c8be13e180252d518bf
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/simplejson?source=hash-mapping
+  size: 133472
+  timestamp: 1758915935656
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/simplejson-3.20.2-py313h6535dbc_0.conda
+  sha256: 71c2198c24807f9efd92497f22dc26d115dd5214feac12e80dbfd92271c7ac6d
+  md5: 02f0dd6760679c4ac13d35465f54d1c9
+  depends:
+  - __osx >=11.0
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/simplejson?source=hash-mapping
+  size: 133973
+  timestamp: 1758916401672
+- conda: https://conda.anaconda.org/conda-forge/win-64/simplejson-3.20.1-py313ha7868ed_0.conda
+  sha256: 0076222af178df4c008d5888f62484bf4998a717f9ae092cfb3cc00ec754c958
+  md5: d366a3d66c2458ad68b3762fe6747e50
+  depends:
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/simplejson?source=hash-mapping
+  size: 131143
+  timestamp: 1739781638812
 - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
   sha256: 41db0180680cc67c3fa76544ffd48d6a5679d96f4b71d7498a759e94edc9a2db
   md5: a451d576819089b0d672f18768be0f65
@@ -9669,8 +9109,8 @@ packages:
   timestamp: 1733381032766
 - pypi: ./
   name: skillmodels
-  version: 0.0.53.dev20+gc65c5e4.d20250716
-  sha256: a08396c482a1ab705a886c1a1d4b60ee18982f6949fa7349395fe13ba8e3e0ae
+  version: 0.0.53.dev24+g5ddfebcdd.d20251006
+  sha256: 8beeebbae1088c9179e331cc67bc4f7ae5b316e638e65fa0b61960f5257c2bce
   requires_dist:
   - dags
   - numpy
@@ -9825,20 +9265,6 @@ packages:
   - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
   - sqlcipher3-binary ; extra == 'sqlcipher'
   requires_python: '>=3.7'
-- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.49.1-h9eae976_2.conda
-  sha256: 85c5b96686a900411ad8bdd424dcc2efb2044f15488bb89e57dff3bfcb74cc65
-  md5: 1894a9d3a8c3ffa53f8ca09fcd2fac25
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=13
-  - libsqlite 3.49.1 hee588c1_2
-  - libzlib >=1.3.1,<2.0a0
-  - ncurses >=6.5,<7.0a0
-  - readline >=8.2,<9.0a0
-  license: Unlicense
-  purls: []
-  size: 859553
-  timestamp: 1742083683966
 - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
   sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41
   md5: b1b505328da7a6b246787df4b5a49fbc
@@ -9884,17 +9310,6 @@ packages:
   purls: []
   size: 151460
   timestamp: 1732982860332
-- conda: https://conda.anaconda.org/conda-forge/noarch/tenacity-9.0.0-pyhd8ed1ab_1.conda
-  sha256: dcf2155fb959773fb102066bfab8e7d79aff67054d142716979274a43fc85735
-  md5: a09f66fe95a54a92172e56a4a97ba271
-  depends:
-  - python >=3.9
-  license: Apache-2.0
-  license_family: APACHE
-  purls:
-  - pkg:pypi/tenacity?source=hash-mapping
-  size: 24808
-  timestamp: 1733649153624
 - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh0d859eb_0.conda
   sha256: b300557c0382478cf661ddb520263508e4b3b5871b471410450ef2846e8c352c
   md5: efba281bbdae5f6b0a1d53c6d4a97c93
diff --git a/src/skillmodels/check_model.py b/src/skillmodels/check_model.py
index f866e15..9a25c49 100644
--- a/src/skillmodels/check_model.py
+++ b/src/skillmodels/check_model.py
@@ -26,10 +26,10 @@ def check_model(model_dict, labels, dimensions, anchoring, has_investments):
 
     """
     report = check_stagemap(
-        stagemap=labels["stagemap"],
-        stages=labels["stages"],
-        n_periods=dimensions["n_periods"],
-        has_investments=has_investments,
+        stagemap=labels["aug_stagemap"],
+        stages=labels["aug_stages"],
+        n_periods=dimensions["n_aug_periods"],
+        is_augmented=has_investments,
     )
     report += _check_anchoring(anchoring)
     invalid_measurements = _check_measurements(model_dict, labels["latent_factors"])
@@ -47,21 +47,21 @@ def check_model(model_dict, labels, dimensions, anchoring, has_investments):
         raise ValueError(f"Invalid model specification: {report}")
 
 
-def check_stagemap(stagemap, stages, n_periods, has_investments):
+def check_stagemap(stagemap, stages, n_periods, is_augmented):
     report = []
-    len_reduction = 2 if has_investments else 1
-    if len(stagemap) != n_periods - len_reduction:
+    step_size = 2 if is_augmented else 1
+    if len(stagemap) != n_periods - step_size:
         report.append(
-            f"The stagemap needs to be of length n_periods - {len_reduction}. "
+            f"The stagemap needs to be of length n_periods - {step_size}. "
             f" n_periods is {n_periods}, the stagemap has length {len(stagemap)}.",
         )
-    if not has_investments:
-        if stages != list(range(len(stages))):
-            report.append(
-                "Stages need to be integers, start at zero and increase by 1."
-            )
+    if stages != list(range(len(stages))):
+        report.append("Stages need to be integers, start at zero and increase by 1.")
 
-        if not np.isin(np.array(stagemap[1:]) - np.array(stagemap[:-1]), (0, 1)).all():
+    # Hijacking the stagemap for endogenous factors leads to interleaved elements.
+    to_consider = [stagemap] if not is_augmented else [stagemap[0::2], stagemap[1::2]]
+    for sm in to_consider:
+        if not np.isin(np.array(sm[1:]) - np.array(sm[:-1]), (0, step_size)).all():
             report.append(
                 "Consecutive entries in stagemap must be equal or increase by 1."
             )
@@ -110,7 +110,7 @@ def _check_measurements(model_dict, factors):
 
 def _check_no_overlap_in_measurements_of_states_and_inv(model_dict, labels):
     report = []
-    for period in labels["periods_raw"]:
+    for period in labels["periods"]:
         meas = {}
         for factor in labels["latent_factors"]:
             props = model_dict["factors"][factor]
diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 160bf51..659ad67 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -48,8 +48,8 @@ def get_constraints_dicts(
     )
     constraints_dicts += _get_mixture_weights_constraints(dimensions["n_mixtures"])
     constraints_dicts += _get_stage_constraints(
-        stagemap=labels["stagemap"],
-        stages=labels["stages"],
+        stagemap=labels["aug_stagemap"],
+        stages=labels["aug_stages"],
     )
     constraints_dicts += _get_constant_factors_constraints(labels=labels)
     constraints_dicts += _get_initial_states_constraints(
@@ -61,7 +61,7 @@ def get_constraints_dicts(
         update_info=update_info,
         controls=labels["controls"],
         anchoring_info=anchoring_info,
-        periods=labels["periods"],
+        periods=labels["aug_periods"],
     )
     if investments_info["has_investments"]:
         constraints_dicts += _get_constraints_for_augmented_periods(
@@ -190,8 +190,8 @@ def _get_stage_constraints(stagemap, stages) -> list[dict]:
     """Equality constraints for transition and shock parameters within stages.
 
     Args:
-        stagemap (list): map periods to stages
-        stages (list): stages
+        stagemap (list): map aug_periods to aug_stages
+        stages (list): aug_stages
     Returns:
         constraints_dicts
 
@@ -202,8 +202,8 @@ def _get_stage_constraints(stagemap, stages) -> list[dict]:
     constraints_dicts = []
 
     stages_to_periods = {stage: [] for stage in stages}
-    for period, stage in enumerate(stagemap):
-        stages_to_periods[stage].append(period)
+    for aug_period, stage in enumerate(stagemap):
+        stages_to_periods[stage].append(aug_period)
 
     for stage, stage_periods in stages_to_periods.items():
         if len(stage_periods) > 1:
@@ -242,10 +242,10 @@ def _get_constant_factors_constraints(labels) -> list[dict]:
     for f, factor in enumerate(labels["latent_factors"]):
         if labels["transition_names"][f] == "constant":
             msg = f"This constraint was generated because {factor} is constant."
-            for period in labels["periods"][:-1]:
+            for aug_period in labels["aug_periods"][:-1]:
                 constraints_dicts.append(
                     {
-                        "loc": ("shock_sds", period, factor, "-"),
+                        "loc": ("shock_sds", aug_period, factor, "-"),
                         "type": "fixed",
                         "value": 0.0,
                         "description": msg,
@@ -300,10 +300,12 @@ def _get_transition_constraints(labels) -> list[dict]:
     for f, factor in enumerate(labels["latent_factors"]):
         tname = labels["transition_names"][f]
         msg = f"This constraint is inherent to the {tname} production function."
-        for period in labels["periods"][:-1]:
+        for aug_period in labels["aug_periods"][:-1]:
             funcname = f"constraints_{tname}"
             if func := getattr(t_f_module, funcname, False):
-                c = func(factor=factor, factors=labels["all_factors"], period=period)
+                c = func(
+                    factor=factor, factors=labels["all_factors"], period=aug_period
+                )
                 if "description" not in c:
                     c["description"] = msg
                 constraints_dicts.append(c)
@@ -397,25 +399,25 @@ def _get_constraints_for_augmented_periods(labels, investments_info) -> list[dic
         tname = labels["transition_names"][f]
         if tname == "constant":
             continue
-        period_type_to_constrain = (
+        aug_period_type_to_constrain = (
             "investments" if investments_info[factor]["is_state"] else "states"
         )
-        periods_to_constrain = [
+        aug_periods_to_constrain = [
             k
-            for k, v in investments_info["periods_to_period_types"].items()
-            if v == period_type_to_constrain
+            for k, v in investments_info["aug_periods_to_aug_period_types"].items()
+            if v == aug_period_type_to_constrain
         ]
-        for period in periods_to_constrain:
+        for aug_period in aug_periods_to_constrain:
             if func := getattr(t_f_module, f"identity_constraints_{tname}", False):
                 constraints_dicts += func(
                     factor=factor,
-                    period=period,
+                    period=aug_period,
                     all_factors=labels["all_factors"],
                 )
-        for period in periods_to_constrain[:-1]:
+        for aug_period in aug_periods_to_constrain[:-1]:
             constraints_dicts.append(
                 {
-                    "loc": ("shock_sds", period, factor, "-"),
+                    "loc": ("shock_sds", aug_period, factor, "-"),
                     "type": "fixed",
                     "value": investments_info["bounds_distance"],
                     "description": "Identity constraint.",
diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index 3dc94b3..e7169a2 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -148,7 +148,7 @@ def get_measurements_corr(data, model_dict, factors, periods):
     periods = _process_periods(periods, model)
     processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = _get_update_info_for_periods_raw(model)
+    update_info = _get_update_info_for_periods(model)
     df = _get_measurement_data(
         data=processed_data,
         update_info=update_info,
@@ -189,7 +189,7 @@ def get_quasi_scores_corr(data, model_dict, factors, periods):
     periods = _process_periods(periods, model)
     processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = _get_update_info_for_periods_raw(model)
+    update_info = _get_update_info_for_periods(model)
     df = _get_quasi_factor_scores_data(
         data=processed_data,
         update_info=update_info,
@@ -273,17 +273,17 @@ def _get_mask(corr, show_upper_triangle, show_diagonal):
     return mask
 
 
-def _get_update_info_for_periods_raw(model):
-    """Transform update_info to use periods_raw instead of periods."""
+def _get_update_info_for_periods(model):
+    """Transform update_info to use user-provided periods instead of augmented periods."""
     update_info = model["update_info"].copy()
 
-    # Replace period level with period_raw using set_codes
-    period_raw_values = update_info.index.get_level_values("period").map(
-        model["labels"]["periods_to_periods_raw"]
+    # Replace period level with user-provided period using set_codes
+    period_values = update_info.index.get_level_values("period").map(
+        model["labels"]["aug_periods_to_periods"]
     )
-    update_info.index = update_info.index.set_codes(period_raw_values, level="period")
+    update_info.index = update_info.index.set_codes(period_values, level="period")
 
-    # Group by period_raw and variable, apply OR logic for boolean columns
+    # Group by period and variable, apply OR logic for boolean columns
     cols = [col for col in update_info.columns if col != "purpose"]
     agg_dict = dict.fromkeys(cols, "any")
     agg_dict["purpose"] = "first"
@@ -627,7 +627,9 @@ def _get_factor_scores_data_for_single_period(
 
     """
     model_periods = [
-        mp for mp, p in model["labels"]["periods_to_periods_raw"].items() if p == period
+        aug_p
+        for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
+        if p == period
     ]
     df = pd.concat(
         [
@@ -780,7 +782,7 @@ def _process_factors(model, factors):
 def _process_periods(periods, model):
     """Process periods to get a list."""
     if periods is None:
-        periods = list(range(model["dimensions"]["n_periods_raw"]))
+        periods = list(range(model["dimensions"]["n_periods"]))
     elif isinstance(periods, int | float):
         periods = [periods]
     return periods
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index ae4d6cf..3e4c364 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -203,18 +203,18 @@ def _partial_some_log_likelihood(
     _periods = pd.Series(update_info.index.get_level_values("period").to_numpy())
     is_predict_iteration = ((_periods - _periods.shift(-1)) == -1).to_numpy()
     # iteration_to_period is used as an indexer to loop over arrays of different lengths
-    # in a jax.lax.scan. It needs to work for arrays of length n_periods and not raise
-    # IndexErrors on tracer arrays of length n_periods - 1 (i.e. n_transitions).
-    # To achieve that, we replace the last period by -1. If there are investments,
-    # the last period is found at index -2 (there should not be measurements for
+    # in a jax.lax.scan. It needs to work for arrays of length n_aug_periods and not raise
+    # IndexErrors on tracer arrays of length n_aug_periods - 1 (i.e. n_transitions).
+    # To achieve that, we replace the last aug_period by -1. If there are investments,
+    # the last aug_period is found at index -2 (there should not be measurements for
     # investments in the "second half" of the last period).
-    last_period = (
-        model["labels"]["periods"][-2]
+    last_aug_period = (
+        model["labels"]["aug_periods"][-2]
         if parsing_info["has_investments"]
-        else model["labels"]["periods"][-1]
+        else model["labels"]["aug_periods"][-1]
     )
-    iteration_to_period = _periods.replace(last_period, -1).to_numpy()
-    assert max(iteration_to_period) == last_period - 1
+    iteration_to_period = _periods.replace(last_aug_period, -1).to_numpy()
+    assert max(iteration_to_period) == last_aug_period - 1
 
     return functools.partial(
         fun,
diff --git a/src/skillmodels/params_index.py b/src/skillmodels/params_index.py
index 42ccd07..29ac6ea 100644
--- a/src/skillmodels/params_index.py
+++ b/src/skillmodels/params_index.py
@@ -33,7 +33,7 @@ def get_params_index(
     )
     ind_tups += get_meas_sds_index_tuples(update_info=update_info)
     ind_tups += get_shock_sds_index_tuples(
-        periods=labels["periods"],
+        periods=labels["aug_periods"],
         factors=labels["latent_factors"],
         has_investments=investments_info["has_investments"],
     )
@@ -48,7 +48,7 @@ def get_params_index(
     )
     ind_tups += get_transition_index_tuples(
         transition_info=transition_info,
-        periods=labels["periods"],
+        periods=labels["aug_periods"],
         has_investments=investments_info["has_investments"],
     )
 
diff --git a/src/skillmodels/parse_params.py b/src/skillmodels/parse_params.py
index 5813cee..b2b7329 100644
--- a/src/skillmodels/parse_params.py
+++ b/src/skillmodels/parse_params.py
@@ -211,14 +211,14 @@ def _get_transition_params(params, info, labels):
     """Create a list of arrays with transition equation parameters."""
     trans_params = {}
     t_info = info["transition"]
-    n_periods = len(labels["periods"])
+    n_aug_periods = len(labels["aug_periods"])
 
     # Use has_investments from parsing_info instead of undefined global
     len_reduction = 2 if info["has_investments"] else 1
 
     for factor in labels["latent_factors"]:
         ilocs = t_info[factor]
-        trans_params[factor] = params[ilocs].reshape(n_periods - len_reduction, -1)
+        trans_params[factor] = params[ilocs].reshape(n_aug_periods - len_reduction, -1)
     return trans_params
 
 
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index a79759d..5d6c94a 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -34,7 +34,7 @@ def process_data(
             n_observed_factors) with data on the observed factors.
 
     """
-    df = pre_process_data(df, labels["periods_raw"])
+    df = pre_process_data(df, labels["periods"])
     df["constant"] = 1
     out = {}
 
@@ -43,7 +43,7 @@ def process_data(
     else:
         df = _add_copies_of_anchoring_outcome(df, anchoring_info)
     _check_data(df, update_info, labels, purpose=purpose)
-    n_obs = int(len(df) / len(labels["periods"]))
+    n_obs = int(len(df) / len(labels["aug_periods"]))
     df = _handle_controls_with_missings(df, labels["controls"], update_info)
     out["controls"] = _generate_controls_array(df, labels, n_obs)
     out["observed_factors"] = _generate_observed_factor_array(df, labels, n_obs)
@@ -86,28 +86,28 @@ def pre_process_data(df, periods):
 
 
 def _get_period_data_for_investments(
+    aug_period: int,
     period: int,
-    period_raw: int,
     df: pd.DataFrame,
     labels: dict[str, Any],
     update_info: pd.DataFrame,
 ) -> pd.DataFrame:
-    meas = _get_period_measurements(update_info, period)
+    meas = _get_period_measurements(update_info, aug_period)
     controls = labels["controls"]
     observed = labels["observed_factors"]
 
-    out = df.query(f"period_raw == {period_raw}")[
+    out = df.query(f"period == {period}")[
         [
             "id",
             *meas,
             *controls,
             *observed,
-            "period_raw",
+            "period",
             "__old_id__",
             "__old_period__",
         ]
     ]
-    out["period"] = period
+    out["aug_period"] = aug_period
     return out
 
 
@@ -121,26 +121,28 @@ def _augment_data_for_investments(
     Endogeneity of investments means that current states influence the
 
     """
-    df = df.reset_index().rename(columns={"period": "period_raw"})
+    df = df.reset_index()
     # Make sure datset is balanced
     n_ids = df["id"].nunique()
-    n_periods = df["period_raw"].nunique()
+    n_periods = df["period"].nunique()
     assert n_ids * n_periods == df.shape[0]
-    assert set(df["period_raw"]) == set(labels["periods_to_periods_raw"].values())
+    assert set(df["period"]) == set(labels["aug_periods_to_periods"].values())
 
     out = pd.concat(
         [
             _get_period_data_for_investments(
+                aug_period=aug_period,
                 period=period,
-                period_raw=period_raw,
                 df=df,
                 update_info=update_info,
                 labels=labels,
             )
-            for period, period_raw in labels["periods_to_periods_raw"].items()
+            for aug_period, period in labels["aug_periods_to_periods"].items()
         ]
     )
-    return out.set_index(["id", "period"]).sort_index()
+    return (
+        out.set_index(["id", "aug_period"]).rename_axis(["id", "period"]).sort_index()
+    )
 
 
 def _add_copies_of_anchoring_outcome(df, anchoring_info):
@@ -153,26 +155,30 @@ def _add_copies_of_anchoring_outcome(df, anchoring_info):
 
 def _check_data(df, update_info, labels, purpose):  # noqa: C901
     var_report = pd.DataFrame(index=update_info.index[:0], columns=["problem"])
-    for period in labels["periods"]:
-        period_data = df.query(f"period == {period}")
+    for aug_period in labels["aug_periods"]:
+        period_data = df.query(f"period == {aug_period}")
         for cont in labels["controls"]:
             if cont not in period_data.columns or period_data[cont].isna().all():
-                var_report.loc[(period, cont), "problem"] = "Variable is missing"
+                var_report.loc[(aug_period, cont), "problem"] = "Variable is missing"
 
         if purpose == "estimation":
-            for meas in _get_period_measurements(update_info, period):
+            for meas in _get_period_measurements(update_info, aug_period):
                 if meas not in period_data.columns:
-                    var_report.loc[(period, meas), "problem"] = "Variable is missing"
+                    var_report.loc[(aug_period, meas), "problem"] = (
+                        "Variable is missing"
+                    )
                 elif len(period_data[meas].dropna().unique()) == 1:
-                    var_report.loc[(period, meas), "problem"] = (
+                    var_report.loc[(aug_period, meas), "problem"] = (
                         "Variable has no variance"
                     )
 
         for factor in labels["observed_factors"]:
             if factor not in period_data.columns:
-                var_report.loc[(period, factor), "problem"] = "Variable is missing"
+                var_report.loc[(aug_period, factor), "problem"] = "Variable is missing"
             elif period_data[factor].isna().any():
-                var_report.loc[(period, factor), "problem"] = "Variable has missings"
+                var_report.loc[(aug_period, factor), "problem"] = (
+                    "Variable has missings"
+                )
 
     var_report = var_report.to_string() if len(var_report) > 0 else ""
 
@@ -215,16 +221,18 @@ def _generate_measurements_array(df, update_info, n_obs):
 
 
 def _generate_controls_array(df, labels, n_obs):
-    arr = np.zeros((len(labels["periods"]), n_obs, len(labels["controls"])))
-    for period in labels["periods"]:
-        arr[period] = df.query(f"period == {period}")[labels["controls"]].to_numpy()
+    arr = np.zeros((len(labels["aug_periods"]), n_obs, len(labels["controls"])))
+    for aug_period in labels["aug_periods"]:
+        arr[aug_period] = df.query(f"period == {aug_period}")[
+            labels["controls"]
+        ].to_numpy()
     return jnp.array(arr, dtype="float32")
 
 
 def _generate_observed_factor_array(df, labels, n_obs):
-    arr = np.zeros((len(labels["periods"]), n_obs, len(labels["observed_factors"])))
-    for period in labels["periods"]:
-        arr[period] = df.query(f"period == {period}")[
+    arr = np.zeros((len(labels["aug_periods"]), n_obs, len(labels["observed_factors"])))
+    for aug_period in labels["aug_periods"]:
+        arr[aug_period] = df.query(f"period == {aug_period}")[
             labels["observed_factors"]
         ].to_numpy()
     return jnp.array(arr, dtype="float32")
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index f0f7ad9..cf67825 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -47,38 +47,38 @@ def process_model(model_dict):
     )
     anchoring = _process_anchoring(model_dict, has_investments)
     if has_investments:
-        model_dict_aug = _augment_periods_for_investments(
+        _model_dict_aug = _augment_periods_for_investments(
             model_dict=model_dict,
             dimensions=dims,
             labels=labels,
         )
         investments_info = _get_investments_info(
             has_investments=has_investments,
-            model_dict=model_dict_aug,
+            model_dict=_model_dict_aug,
             labels=labels,
             bounds_distance=model_dict["estimation_options"]["bounds_distance"],
         )
     else:
-        model_dict_aug = model_dict
+        _model_dict_aug = model_dict
         investments_info = {"has_investments": has_investments}
     check_model(
-        model_dict=model_dict_aug,
+        model_dict=_model_dict_aug,
         labels=labels,
         dimensions=dims,
         anchoring=anchoring,
         has_investments=has_investments,
     )
-    transition_info = _get_transition_info(model_dict_aug, labels)
+    transition_info = _get_transition_info(_model_dict_aug, labels)
     labels["transition_names"] = list(transition_info["function_names"].values())
 
     processed = {
         "dimensions": dims,
         "labels": labels,
         "anchoring": anchoring,
-        "estimation_options": _process_estimation_options(model_dict_aug),
+        "estimation_options": _process_estimation_options(_model_dict_aug),
         "transition_info": transition_info,
-        "update_info": _get_update_info(model_dict_aug, dims, labels, anchoring),
-        "normalizations": _process_normalizations(model_dict_aug, dims, labels),
+        "update_info": _get_update_info(_model_dict_aug, dims, labels, anchoring),
+        "normalizations": _process_normalizations(_model_dict_aug, dims, labels),
         "investments_info": investments_info,
     }
     return processed
@@ -122,27 +122,31 @@ def get_dimensions(model_dict, has_investments):
 
     """
     all_n_periods = [len(d["measurements"]) for d in model_dict["factors"].values()]
-    n_periods_raw = max(all_n_periods)
-    n_periods = 2 * n_periods_raw if has_investments else n_periods_raw
+    n_periods = max(all_n_periods)
+    n_aug_periods = 2 * n_periods if has_investments else n_periods
 
     dims = {
         "n_latent_factors": len(model_dict["factors"]),
         "n_observed_factors": len(model_dict.get("observed_factors", [])),
-        "n_periods": n_periods,
-        "n_periods_raw": n_periods_raw,
         "n_controls": len(model_dict.get("controls", [])) + 1,  # plus 1: constant
         "n_mixtures": model_dict["estimation_options"].get("n_mixtures", 1),
+        "n_aug_periods": n_aug_periods,
+        "n_periods": n_periods,
     }
     dims["n_all_factors"] = dims["n_latent_factors"] + dims["n_observed_factors"]
     return dims
 
 
-def _get_periods_to_periods_raw(
-    n_periods: int, has_investments: bool
+def _get_aug_periods_to_periods(
+    n_aug_periods: int, has_investments: bool
 ) -> dict[int, int]:
-    """Return mapper of periods potentially augmented for investments to raw periods."""
-    periods = list(range(n_periods))
-    return {p: p // 2 for p in periods} if has_investments else {p: p for p in periods}
+    """Return mapper of (potentially) augmented periods to user-provided periods."""
+    aug_periods = list(range(n_aug_periods))
+    return (
+        {p: p // 2 for p in aug_periods}
+        if has_investments
+        else {p: p for p in aug_periods}
+    )
 
 
 def _get_labels(model_dict, has_investments, dimensions):
@@ -159,50 +163,48 @@ def _get_labels(model_dict, has_investments, dimensions):
         factors, periods, controls, stagemap and stages. See :ref:`labels`
 
     """
-    periods_to_periods_raw = _get_periods_to_periods_raw(
-        n_periods=dimensions["n_periods"],
+    aug_periods_to_periods = _get_aug_periods_to_periods(
+        n_aug_periods=dimensions["n_aug_periods"],
         has_investments=has_investments,
     )
 
-    stagemap_raw = model_dict.get(
-        "stagemap", list(range(dimensions["n_periods_raw"] - 1))
-    )
-    stages_raw = sorted(int(v) for v in np.unique(stagemap_raw))
+    stagemap = model_dict.get("stagemap", list(range(dimensions["n_periods"] - 1)))
+    stages = sorted(int(v) for v in np.unique(stagemap))
 
+    report = check_stagemap(
+        stagemap=stagemap,
+        stages=stages,
+        n_periods=dimensions["n_periods"],
+        is_augmented=False,
+    )
+    if report:
+        raise ValueError(f"Invalid stage map: {report}")
     if has_investments:
-        report = check_stagemap(
-            stagemap=stagemap_raw,
-            stages=stages_raw,
-            n_periods=dimensions["n_periods_raw"],
-            has_investments=False,
-        )
-        if report:
-            raise ValueError(f"Invalid stage map: {report}")
-        stagemap = []
-        stages_to_stages_raw = {}
-        relevant_periods = sorted(periods_to_periods_raw.keys())[:-2]
-        for p in relevant_periods:
-            p_raw = periods_to_periods_raw[p]
-            s_raw = stagemap_raw[p_raw]
-            s = 2 * s_raw + p % 2
-            stagemap.append(s)
-            stages_to_stages_raw[s] = s_raw
+        aug_stagemap = []
+        aug_stages_to_stages = {}
+        relevant_aug_periods = sorted(aug_periods_to_periods.keys())[:-2]
+        for aug_p in relevant_aug_periods:
+            p = aug_periods_to_periods[aug_p]
+            s = stagemap[p]
+            aug_s = 2 * s + aug_p % 2
+            aug_stagemap.append(aug_s)
+            aug_stages_to_stages[aug_s] = s
     else:
-        stagemap = stagemap_raw
-        stages_to_stages_raw = {s_raw: s_raw for s_raw in stages_raw}
+        aug_stagemap = stagemap
+        aug_stages_to_stages = {s: s for s in stages}
 
     labels = {
         "latent_factors": list(model_dict["factors"]),
         "observed_factors": list(model_dict.get("observed_factors", [])),
         "controls": ["constant", *list(model_dict.get("controls", []))],
-        "periods": list(periods_to_periods_raw.keys()),
-        "periods_raw": sorted(set(periods_to_periods_raw.values())),
-        "periods_to_periods_raw": periods_to_periods_raw,
+        "periods": sorted(set(aug_periods_to_periods.values())),
         "stagemap": stagemap,
-        "stages": sorted(int(v) for v in np.unique(stagemap)),
-        "stages_to_stages_raw": stages_to_stages_raw,
-        "stagemap_raw": stagemap_raw,
-        "stages_raw": stages_raw,
+        "stages": stages,
+        "aug_periods": list(aug_periods_to_periods.keys()),
+        "aug_periods_to_periods": aug_periods_to_periods,
+        "aug_stagemap": aug_stagemap,
+        "aug_stages": sorted(int(v) for v in np.unique(aug_stagemap)),
+        "aug_stages_to_stages": aug_stages_to_stages,
     }
 
     labels["all_factors"] = labels["latent_factors"] + labels["observed_factors"]
@@ -269,10 +271,10 @@ def _process_anchoring(model_dict, has_investments):
     return anchinfo
 
 
-def _insert_empty_elements_into_list(old, insert_at_modulo, to_insert, p_to_p_raw):
+def _insert_empty_elements_into_list(old, insert_at_modulo, to_insert, aug_p_to_p):
     return [
-        to_insert if p % 2 == insert_at_modulo else old[p_raw]
-        for p, p_raw in p_to_p_raw.items()
+        to_insert if aug_p % 2 == insert_at_modulo else old[p]
+        for aug_p, p in aug_p_to_p.items()
     ]
 
 
@@ -297,21 +299,21 @@ def _augment_periods_for_investments(
         insert_at_modulo = 0 if v.get("is_investment", False) else 1
 
         # Insert empty elements into measurements when we do not have those.
-        if len(v["measurements"]) != dimensions["n_periods_raw"]:
+        if len(v["measurements"]) != dimensions["n_periods"]:
             raise ValueError(
-                "Measurements must be of length `n_periods_raw`, "
+                "Measurements must be of length `n_periods`, "
                 f"got {v['measurements']} for {fac}"
             )
         aug["factors"][fac]["measurements"] = _insert_empty_elements_into_list(
             old=v["measurements"],
             insert_at_modulo=insert_at_modulo,
             to_insert=[],
-            p_to_p_raw=labels["periods_to_periods_raw"],
+            aug_p_to_p=labels["aug_periods_to_periods"],
         )
 
         # Insert empty elements into normalizations when we do not have those.
         for norm_type, normalizations in v.get("normalizations", {}).items():
-            if not len(normalizations) == dimensions["n_periods_raw"]:
+            if not len(normalizations) == dimensions["n_periods"]:
                 raise ValueError(
                     "Normalizations must be lists of length `n_periods`, "
                     f"got {normalizations} for {fac}['normalizations']['{norm_type}']"
@@ -321,7 +323,7 @@ def _augment_periods_for_investments(
                     old=normalizations,
                     insert_at_modulo=insert_at_modulo,
                     to_insert={},
-                    p_to_p_raw=labels["periods_to_periods_raw"],
+                    aug_p_to_p=labels["aug_periods_to_periods"],
                 )
             )
     return aug
@@ -404,8 +406,8 @@ def _get_investments_info(
     """Collect information about investments."""
     investments_info = {
         "has_investments": has_investments,
-        "periods_to_period_types": _get_periods_to_period_types(
-            periods=labels["periods_to_periods_raw"].keys(),
+        "aug_periods_to_aug_period_types": _get_aug_periods_to_aug_period_types(
+            aug_periods=labels["aug_periods_to_periods"].keys(),
             has_investments=has_investments,
         ),
         "bounds_distance": bounds_distance,
@@ -421,14 +423,14 @@ def _get_investments_info(
     return investments_info
 
 
-def _get_periods_to_period_types(
-    periods: list[int], has_investments: bool
+def _get_aug_periods_to_aug_period_types(
+    aug_periods: list[int], has_investments: bool
 ) -> dict[int, Literal["states", "investments"]]:
     return {
-        p: ("states" if p % 2 == 0 else "investments")
+        aug_p: ("states" if aug_p % 2 == 0 else "investments")
         if has_investments
-        else {p: "states"}
-        for p in periods
+        else {aug_p: "states"}
+        for aug_p in aug_periods
     }
 
 
@@ -454,22 +456,22 @@ def _get_update_info(model_dict, dimensions, labels, anchoring_info):
     measurements = {}
     for factor in labels["latent_factors"]:
         measurements[factor] = model_dict["factors"][factor]["measurements"]
-        if len(measurements[factor]) != dimensions["n_periods"]:
+        if len(measurements[factor]) != dimensions["n_aug_periods"]:
             raise ValueError(
-                "Measurements must be of length `n_periods`, "
+                "Measurements must be of length `n_aug_periods`, "
                 f"got {measurements[factor]} for {factor}"
             )
 
-    for period in labels["periods"]:
+    for aug_period in labels["aug_periods"]:
         for factor in labels["latent_factors"]:
-            for meas in measurements[factor][period]:
-                uinfo.loc[(period, meas), factor] = True
-                uinfo.loc[(period, meas), "purpose"] = "measurement"
+            for meas in measurements[factor][aug_period]:
+                uinfo.loc[(aug_period, meas), factor] = True
+                uinfo.loc[(aug_period, meas), "purpose"] = "measurement"
         for factor in anchoring_info["factors"]:
             outcome = anchoring_info["outcomes"][factor]
             name = f"{outcome}_{factor}"
-            uinfo.loc[(period, name), factor] = True
-            uinfo.loc[(period, name), "purpose"] = "anchoring"
+            uinfo.loc[(aug_period, name), factor] = True
+            uinfo.loc[(aug_period, name), "purpose"] = "anchoring"
 
     for col in [c for c in uinfo.columns if c != "purpose"]:
         uinfo[col] = uinfo[col].fillna(value=False).astype(bool)
@@ -497,11 +499,11 @@ def _process_normalizations(model_dict, dimensions, labels):
         norminfo = model_dict["factors"][factor].get("normalizations", {})
         for norm_type in ["loadings", "intercepts"]:
             candidate = norminfo.get(
-                norm_type, [{} for _ in range(dimensions["n_periods"])]
+                norm_type, [{} for _ in range(dimensions["n_aug_periods"])]
             )
-            if not len(candidate) == dimensions["n_periods"]:
+            if not len(candidate) == dimensions["n_aug_periods"]:
                 raise ValueError(
-                    "Normalizations must be of length `n_periods`, "
+                    "Normalizations must be of length `n_aug_periods`, "
                     f"got {norminfo} for {factor}['{norm_type}']"
                 )
             normalizations[factor][norm_type] = candidate
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index bfdce81..3cdb41e 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -171,9 +171,9 @@ def _simulate_dataset(
 
     n_states = dimensions["n_latent_factors"]
     if has_investments:
-        n_periods = dimensions["n_periods"] - 1
+        n_periods = dimensions["n_aug_periods"] - 2
     else:
-        n_periods = dimensions["n_periods"]
+        n_periods = dimensions["n_aug_periods"]
 
     weights = np.exp(log_weights)[0]
     loadings_df = pd.DataFrame(
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 229510d..9fdc0e3 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -388,19 +388,19 @@ def _get_states_data(model, period, data, states, observed_factors):
         # convert from jax to numpy
         _observed_arr = np.array(_observed_arr)
         if model["investments_info"]["has_investments"]:
-            both_periods = {
-                p: period
-                for p, pr in model["labels"]["periods_to_periods_raw"].items()
-                if pr == period
-            }
+            both_aug_periods = [
+                aug_p
+                for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
+                if p == period
+            ]
             to_concat = []
-            for p in both_periods:
+            for aug_p in both_aug_periods:
                 df = pd.DataFrame(
-                    data=_observed_arr[p],
+                    data=_observed_arr[aug_p],
                     columns=observed_factors,
                 )
                 df["id"] = df.index
-                df["period"] = p
+                df["period"] = aug_p
                 to_concat.append(df)
             observed_data = pd.concat(to_concat)
         else:
@@ -483,9 +483,9 @@ def _prepare_data_for_one_plot_average_2d(
 ):
     if model["investments_info"]["has_investments"]:
         aug_periods = [
-            p
-            for p, pr in model["labels"]["periods_to_periods_raw"].items()
-            if pr == period
+            aug_p
+            for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
+            if p == period
         ]
     else:
         aug_periods = [period]
diff --git a/tests/simplest_augmented_data_expected.csv b/tests/simplest_augmented_data_expected.csv
index 5065e5c..5f0b1a6 100644
--- a/tests/simplest_augmented_data_expected.csv
+++ b/tests/simplest_augmented_data_expected.csv
@@ -1,13 +1,13 @@
-id,period,period_raw,constant,var,inv,of
-0,0,0,1,0.0,,10.0
-0,1,0,1,,5.0,10.0
-0,2,1,1,,,
-0,3,1,1,,,
-1,0,0,1,1.0,,11.0
-1,1,0,1,,6.0,11.0
-1,2,1,1,2.0,,12.0
-1,3,1,1,,7.0,12.0
-2,0,0,1,3.0,,13.0
-2,1,0,1,,8.0,13.0
-2,2,1,1,4.0,,14.0
-2,3,1,1,,9.0,14.0
+id,period,constant,var,inv,of
+0,0,1,0.0,,10.0
+0,1,1,,5.0,10.0
+0,2,1,,,
+0,3,1,,,
+1,0,1,1.0,,11.0
+1,1,1,,6.0,11.0
+1,2,1,2.0,,12.0
+1,3,1,,7.0,12.0
+2,0,1,3.0,,13.0
+2,1,1,,8.0,13.0
+2,2,1,4.0,,14.0
+2,3,1,,9.0,14.0
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index 9839f3b..9e2714e 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -174,7 +174,7 @@ def test_stage_constraints_with_investments():
 def test_constant_factor_constraints():
     labels = {
         "latent_factors": ["fac1", "fac2"],
-        "periods": [0, 1, 2],
+        "aug_periods": [0, 1, 2],
         "transition_names": ["bla", "constant"],
     }
 
@@ -220,7 +220,7 @@ def test_trans_coeff_constraints():
     labels = {
         "latent_factors": ["fac1", "fac2", "fac3"],
         "transition_names": ["log_ces", "bla", "blubb"],
-        "periods": [0, 1, 2],
+        "aug_periods": [0, 1, 2],
     }
     labels["all_factors"] = labels["latent_factors"]
 
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index ffb7e6a..502a91f 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -63,7 +63,7 @@ def simplest_augmented():
 def test_augment_data_for_investments(simplest_augmented):
     model = process_model(simplest_augmented["model_dict"])
     pre_processed_data = pre_process_data(
-        simplest_augmented["data_input"], model["labels"]["periods_raw"]
+        simplest_augmented["data_input"], model["labels"]["periods"]
     )
     pre_processed_data["constant"] = 1
     res = _augment_data_for_investments(
@@ -71,7 +71,7 @@ def test_augment_data_for_investments(simplest_augmented):
         labels=model["labels"],
         update_info=model["update_info"],
     )
-    cols = ["period_raw", "var", "inv", "constant", "of"]
+    cols = ["var", "inv", "constant", "of"]
     pd.testing.assert_frame_equal(res[cols], simplest_augmented["data_exp"][cols])
 
 
@@ -121,7 +121,7 @@ def test_generate_controls_array():
     """
     data = _read_csv_string(csv, ["id", "period"])
 
-    labels = {"controls": ["c1", "c2"], "periods": [0, 1]}
+    labels = {"controls": ["c1", "c2"], "aug_periods": [0, 1]}
 
     calculated = _generate_controls_array(data, labels, 2)
     expected = jnp.array([[[1, 2], [5, 8]], [[3, 4], [7, 8]]])
@@ -138,7 +138,7 @@ def test_generate_observed_factor_array():
     """
     data = _read_csv_string(csv, ["id", "period"])
 
-    labels = {"observed_factors": ["v1", "v2"], "periods": [0, 1]}
+    labels = {"observed_factors": ["v1", "v2"], "aug_periods": [0, 1]}
 
     calculated = _generate_observed_factor_array(data, labels, 2)
     expected = jnp.array([[[1, 2], [5, 8]], [[3, 4], [7, 8]]])
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index aed0228..6cbad11 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -133,7 +133,7 @@ def test_anchoring_and_investments_conflict():
     model_dict["factors"]["fac3"]["is_investment"] = True
     del model_dict["stagemap"]
     with pytest.raises(
-        ValueError, match="anchoring is not supported when investments are present."
+        ValueError, match=r"anchoring is not supported when investments are present."
     ):
         process_model(model_dict)
 
@@ -157,9 +157,9 @@ def test_stagemap_with_investments():
     model_dict["stagemap"] = [0, 0, 1, 1, 2, 2, 3]
     del model_dict["anchoring"]
     model = process_model(model_dict)
-    assert model["labels"]["stagemap_raw"] == model_dict["stagemap"]
-    assert model["labels"]["stages_raw"] == [0, 1, 2, 3]
-    assert model["labels"]["stagemap"] == [0, 1, 0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7]
+    assert model["labels"]["stagemap"] == model_dict["stagemap"]
+    assert model["labels"]["stages"] == [0, 1, 2, 3]
+    assert model["labels"]["aug_stagemap"] == [0, 1, 0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7]
 
 
 @pytest.fixture
@@ -182,23 +182,23 @@ def test_with_inv_dimensions(model2_inv):
     assert res["n_latent_factors"] == 3
     assert res["n_observed_factors"] == 0
     assert res["n_all_factors"] == 3
-    assert res["n_periods"] == 16
-    assert res["n_periods_raw"] == 8
+    assert res["n_aug_periods"] == 16
+    assert res["n_periods"] == 8
     assert res["n_controls"] == 2
     assert res["n_mixtures"] == 1
 
 
 def test_with_inv_labels(model2_inv):
     res = process_model(model2_inv)["labels"]
-    n_periods = 16
+    n_aug_periods = 16
     assert res["latent_factors"] == ["fac1", "fac2", "fac3"]
     assert res["observed_factors"] == []
     assert res["all_factors"] == ["fac1", "fac2", "fac3"]
     assert res["controls"] == ["constant", "x1"]
-    assert res["periods"] == list(range(n_periods))
-    assert res["periods_raw"] == [0, 1, 2, 3, 4, 5, 6, 7]
-    assert res["stagemap"] == list(range(n_periods - 2))
-    assert res["stages"] == list(range(n_periods - 2))
+    assert res["aug_periods"] == list(range(n_aug_periods))
+    assert res["periods"] == [0, 1, 2, 3, 4, 5, 6, 7]
+    assert res["aug_stagemap"] == list(range(n_aug_periods - 2))
+    assert res["aug_stages"] == list(range(n_aug_periods - 2))
 
 
 def test_with_inv_estimation_options(model2_inv):

From c802a1590c0f5a7ec97e04fc924a5b0bfbbe9cc1 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 6 Oct 2025 13:17:37 +0200
Subject: [PATCH 37/48] Implement Janos' suggestion to only ever talk of
 endogenous factors.

---
 src/skillmodels/check_model.py                |  18 +--
 src/skillmodels/constraints.py                |  27 +++--
 src/skillmodels/correlation_heatmap.py        |   2 +-
 src/skillmodels/filtered_states.py            |   6 +-
 src/skillmodels/maximization_inputs.py        |  20 ++--
 src/skillmodels/params_index.py               |  18 +--
 src/skillmodels/parse_params.py               |  14 ++-
 src/skillmodels/process_data.py               |  25 ++--
 src/skillmodels/process_model.py              | 112 ++++++++++--------
 src/skillmodels/simulate_data.py              |  18 ++-
 src/skillmodels/utilities.py                  |  40 ++++---
 .../visualize_transition_equations.py         |  14 ++-
 ...model2_with_endog_correct_update_info.csv} |   0
 tests/simplest_augmented_model.yaml           |   4 +-
 tests/test_constraints.py                     |   4 +-
 tests/test_params_index.py                    |  10 +-
 tests/test_parse_params.py                    |   2 +-
 tests/test_process_data.py                    |   6 +-
 tests/test_process_model.py                   |  93 ++++++++-------
 19 files changed, 244 insertions(+), 189 deletions(-)
 rename tests/{model2_with_inv_correct_update_info.csv => model2_with_endog_correct_update_info.csv} (100%)

diff --git a/src/skillmodels/check_model.py b/src/skillmodels/check_model.py
index 9a25c49..41dc1e4 100644
--- a/src/skillmodels/check_model.py
+++ b/src/skillmodels/check_model.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 
-def check_model(model_dict, labels, dimensions, anchoring, has_investments):
+def check_model(model_dict, labels, dimensions, anchoring, has_endogenous_factors):
     """Check consistency and validity of the model specification.
 
     labels, dimensions and anchoring information are done before the model checking
@@ -19,7 +19,7 @@ def check_model(model_dict, labels, dimensions, anchoring, has_investments):
             factors, periods, controls, stagemap and stages. See :ref:`labels`
         anchoring (dict): Dictionary with information about anchoring.
             See :ref:`anchoring`
-        has_investments (bool): Whether the model has any investment factors
+        has_endogenous_factors (bool): Whether the model has any endogenous factors
 
     Raises:
         ValueError
@@ -29,13 +29,13 @@ def check_model(model_dict, labels, dimensions, anchoring, has_investments):
         stagemap=labels["aug_stagemap"],
         stages=labels["aug_stages"],
         n_periods=dimensions["n_aug_periods"],
-        is_augmented=has_investments,
+        is_augmented=has_endogenous_factors,
     )
     report += _check_anchoring(anchoring)
     invalid_measurements = _check_measurements(model_dict, labels["latent_factors"])
     if invalid_measurements:
         report += invalid_measurements
-    elif has_investments:
+    elif has_endogenous_factors:
         # Make this conditional because the check only works for valid meas.
         report += _check_no_overlap_in_measurements_of_states_and_inv(
             model_dict, labels
@@ -114,14 +114,14 @@ def _check_no_overlap_in_measurements_of_states_and_inv(model_dict, labels):
         meas = {}
         for factor in labels["latent_factors"]:
             props = model_dict["factors"][factor]
-            if props.get("is_investment", False):
-                meas["investments"] = set(props["measurements"][period])
+            if props.get("is_endogenous", False):
+                meas["endogenous_factors"] = set(props["measurements"][period])
             else:
                 meas["states"] = set(props["measurements"][period])
-        if overlap := meas["states"].intersection(meas["investments"]):
+        if overlap := meas["states"].intersection(meas["endogenous_factors"]):
             report.append(
-                "Measurements for states and investments must not overlap. Check "
-                f"measurements {overlap} in period {period}.",
+                "Measurements for exogenous and endogenous latent factors must not "
+                f"overlap.\n\nCheck measurements {overlap} in period {period}.",
             )
     return report
 
diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 659ad67..6a96915 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -13,7 +13,12 @@
 
 
 def get_constraints_dicts(
-    dimensions, labels, anchoring_info, update_info, normalizations, investments_info
+    dimensions,
+    labels,
+    anchoring_info,
+    update_info,
+    normalizations,
+    endogenous_factors_info,
 ) -> list[dict]:
     """Generate constraints implied by the model specification.
 
@@ -63,10 +68,10 @@ def get_constraints_dicts(
         anchoring_info=anchoring_info,
         periods=labels["aug_periods"],
     )
-    if investments_info["has_investments"]:
+    if endogenous_factors_info["has_endogenous_factors"]:
         constraints_dicts += _get_constraints_for_augmented_periods(
             labels=labels,
-            investments_info=investments_info,
+            endogenous_factors_info=endogenous_factors_info,
         )
 
     for i, c in enumerate(constraints_dicts):
@@ -377,11 +382,13 @@ def _get_anchoring_constraints(
     return constraints_dicts
 
 
-def _get_constraints_for_augmented_periods(labels, investments_info) -> list[dict]:
+def _get_constraints_for_augmented_periods(
+    labels, endogenous_factors_info
+) -> list[dict]:
     """Constraints for augmented periods.
 
     - Carry forward states from uneven periods to even periods
-    - Carry forward investments even periods to uneven periods
+    - Carry forward endogenous factors even periods to uneven periods
     - Set shock_sds to 0 when carrying anything forward
 
     Both depend on the transition function.
@@ -400,11 +407,15 @@ def _get_constraints_for_augmented_periods(labels, investments_info) -> list[dic
         if tname == "constant":
             continue
         aug_period_type_to_constrain = (
-            "investments" if investments_info[factor]["is_state"] else "states"
+            "endogenous_factors"
+            if endogenous_factors_info[factor]["is_state"]
+            else "states"
         )
         aug_periods_to_constrain = [
             k
-            for k, v in investments_info["aug_periods_to_aug_period_types"].items()
+            for k, v in endogenous_factors_info[
+                "aug_periods_to_aug_period_types"
+            ].items()
             if v == aug_period_type_to_constrain
         ]
         for aug_period in aug_periods_to_constrain:
@@ -419,7 +430,7 @@ def _get_constraints_for_augmented_periods(labels, investments_info) -> list[dic
                 {
                     "loc": ("shock_sds", aug_period, factor, "-"),
                     "type": "fixed",
-                    "value": investments_info["bounds_distance"],
+                    "value": endogenous_factors_info["bounds_distance"],
                     "description": "Identity constraint.",
                 }
             )
diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index e7169a2..84566de 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -607,7 +607,7 @@ def _get_factor_scores_data_for_single_period(
 ):
     """Get frame with factor scores in a given period.
 
-    Careful: When we have investments, *period* refers to the raw period, but the
+    Careful: When we have endogenous factors, *period* refers to the raw period, but the
     *params* are for the augmented periods. This function is the layer to abstract from
     augmented periods.
 
diff --git a/src/skillmodels/filtered_states.py b/src/skillmodels/filtered_states.py
index 30c542b..cbf51f9 100644
--- a/src/skillmodels/filtered_states.py
+++ b/src/skillmodels/filtered_states.py
@@ -61,7 +61,7 @@ def anchor_states_df(states_df, model_dict, params):
         labels=model["labels"],
         dimensions=model["dimensions"],
         transition_info=model["transition_info"],
-        investments_info=model["investments_info"],
+        endogenous_factors_info=model["endogenous_factors_info"],
     )
 
     params = params.loc[p_index]
@@ -71,7 +71,9 @@ def anchor_states_df(states_df, model_dict, params):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
-        has_investments=model["investments_info"]["has_investments"],
+        has_endogenous_factors=model["endogenous_factors_info"][
+            "has_endogenous_factors"
+        ],
     )
 
     *_, pardict = parse_params(
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 3e4c364..574515f 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -51,7 +51,7 @@ def get_maximization_inputs(model_dict, data, split_dataset=1):
             bounds. The value column is empty except for the fixed constraints, which
             are set including the bounds.
         data_aug (pd.DataFrame): DataFrame with augmented data. If model contains
-            investment factors, we double up the number of periods in order to add
+            endogenous factors, we double up the number of periods in order to add
 
 
 
@@ -62,7 +62,7 @@ def get_maximization_inputs(model_dict, data, split_dataset=1):
         labels=model["labels"],
         dimensions=model["dimensions"],
         transition_info=model["transition_info"],
-        investments_info=model["investments_info"],
+        endogenous_factors_info=model["endogenous_factors_info"],
     )
 
     parsing_info = create_parsing_info(
@@ -70,11 +70,15 @@ def get_maximization_inputs(model_dict, data, split_dataset=1):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
-        has_investments=model["investments_info"]["has_investments"],
+        has_endogenous_factors=model["endogenous_factors_info"][
+            "has_endogenous_factors"
+        ],
     )
     processed_data = process_data(
         df=data,
-        has_investments=model["investments_info"]["has_investments"],
+        has_endogenous_factors=model["endogenous_factors_info"][
+            "has_endogenous_factors"
+        ],
         labels=model["labels"],
         update_info=model["update_info"],
         anchoring_info=model["anchoring"],
@@ -159,7 +163,7 @@ def debug_loglike(params):
         anchoring_info=model["anchoring"],
         update_info=model["update_info"],
         normalizations=model["normalizations"],
-        investments_info=model["investments_info"],
+        endogenous_factors_info=model["endogenous_factors_info"],
     )
 
     constraints = constraints_dicts_to_om(_constraints_dicts)
@@ -205,12 +209,12 @@ def _partial_some_log_likelihood(
     # iteration_to_period is used as an indexer to loop over arrays of different lengths
     # in a jax.lax.scan. It needs to work for arrays of length n_aug_periods and not raise
     # IndexErrors on tracer arrays of length n_aug_periods - 1 (i.e. n_transitions).
-    # To achieve that, we replace the last aug_period by -1. If there are investments,
+    # To achieve that, we replace the last aug_period by -1. If there are endogenous factors,
     # the last aug_period is found at index -2 (there should not be measurements for
-    # investments in the "second half" of the last period).
+    # endogenous factors in the "second half" of the last period).
     last_aug_period = (
         model["labels"]["aug_periods"][-2]
-        if parsing_info["has_investments"]
+        if parsing_info["has_endogenous_factors"]
         else model["labels"]["aug_periods"][-1]
     )
     iteration_to_period = _periods.replace(last_aug_period, -1).to_numpy()
diff --git a/src/skillmodels/params_index.py b/src/skillmodels/params_index.py
index 29ac6ea..1445f3c 100644
--- a/src/skillmodels/params_index.py
+++ b/src/skillmodels/params_index.py
@@ -2,7 +2,7 @@
 
 
 def get_params_index(
-    update_info, labels, dimensions, transition_info, investments_info
+    update_info, labels, dimensions, transition_info, endogenous_factors_info
 ):
     """Generate index for the params_df for optimagic.
 
@@ -19,7 +19,7 @@ def get_params_index(
         options (dict): Tuning parameters for the estimation.
             See :ref:`estimation_options`.
         transition_info (dict): Information about the transition equations.
-        investments_info (dict): Information about the investment factors, if any.
+        endogenous_factors_info (dict): Information about the endogenous factors, if any.
 
     Returns:
         params_index (pd.MultiIndex)
@@ -35,7 +35,7 @@ def get_params_index(
     ind_tups += get_shock_sds_index_tuples(
         periods=labels["aug_periods"],
         factors=labels["latent_factors"],
-        has_investments=investments_info["has_investments"],
+        has_endogenous_factors=endogenous_factors_info["has_endogenous_factors"],
     )
     ind_tups += initial_mean_index_tuples(
         n_mixtures=dimensions["n_mixtures"],
@@ -49,7 +49,7 @@ def get_params_index(
     ind_tups += get_transition_index_tuples(
         transition_info=transition_info,
         periods=labels["aug_periods"],
-        has_investments=investments_info["has_investments"],
+        has_endogenous_factors=endogenous_factors_info["has_endogenous_factors"],
     )
 
     index = pd.MultiIndex.from_tuples(
@@ -114,7 +114,7 @@ def get_meas_sds_index_tuples(update_info):
     return ind_tups
 
 
-def get_shock_sds_index_tuples(periods, factors, has_investments):
+def get_shock_sds_index_tuples(periods, factors, has_endogenous_factors):
     """Index tuples for shock_sd.
 
     Args:
@@ -125,7 +125,7 @@ def get_shock_sds_index_tuples(periods, factors, has_investments):
         ind_tups (list)
 
     """
-    end = -2 if has_investments else -1
+    end = -2 if has_endogenous_factors else -1
     ind_tups = []
     for period in periods[:end]:
         for factor in factors:
@@ -194,7 +194,7 @@ def get_initial_cholcovs_index_tuples(n_mixtures, factors):
     return ind_tups
 
 
-def get_transition_index_tuples(transition_info, periods, has_investments):
+def get_transition_index_tuples(transition_info, periods, has_endogenous_factors):
     """Index tuples for transition equation coefficients.
 
     Args:
@@ -202,13 +202,13 @@ def get_transition_index_tuples(transition_info, periods, has_investments):
         all_factors (list): The latent and observed factors of the model.
         periods (list): The periods of the model
         transition_names (list): name of the transition equation of each factor
-        has_investments (bool): Whether the model has investment factors.
+        has_endogenous_factors (bool): Whether the model has endogenous factors.
 
     Returns:
         ind_tups (list)
 
     """
-    end = -2 if has_investments else -1
+    end = -2 if has_endogenous_factors else -1
     ind_tups = []
     for factor, names in transition_info["param_names"].items():
         for period in periods[:end]:
diff --git a/src/skillmodels/parse_params.py b/src/skillmodels/parse_params.py
index b2b7329..7e2e819 100644
--- a/src/skillmodels/parse_params.py
+++ b/src/skillmodels/parse_params.py
@@ -5,7 +5,9 @@
 import pandas as pd
 
 
-def create_parsing_info(params_index, update_info, labels, anchoring, has_investments):
+def create_parsing_info(
+    params_index, update_info, labels, anchoring, has_endogenous_factors
+):
     """Create a dictionary with information how the parameter vector has to be parsed.
 
     Args:
@@ -16,7 +18,7 @@ def create_parsing_info(params_index, update_info, labels, anchoring, has_invest
         labels (dict): Dict of lists with labels for the model quantities like
             factors, periods, controls, stagemap and stages. See :ref:`labels`
         anchoring (dict): Dictionary with anchoring settings.
-        has_investments (bool): Whether the model includes investments.
+        has_endogenous_factors (bool): Whether the model includes endogenous factors.
 
     Returns:
         dict: dictionary that maps model quantities to positions or slices of the
@@ -75,8 +77,8 @@ def create_parsing_info(params_index, update_info, labels, anchoring, has_invest
         "ignore_constant_when_anchoring"
     ]
 
-    # Add has_investments to parsing_info
-    parsing_info["has_investments"] = has_investments
+    # Add has_endogenous_factors to parsing_info
+    parsing_info["has_endogenous_factors"] = has_endogenous_factors
 
     return parsing_info
 
@@ -213,8 +215,8 @@ def _get_transition_params(params, info, labels):
     t_info = info["transition"]
     n_aug_periods = len(labels["aug_periods"])
 
-    # Use has_investments from parsing_info instead of undefined global
-    len_reduction = 2 if info["has_investments"] else 1
+    # Use has_endogenous_factors from parsing_info instead of undefined global
+    len_reduction = 2 if info["has_endogenous_factors"] else 1
 
     for factor in labels["latent_factors"]:
         ilocs = t_info[factor]
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 5d6c94a..87b5b75 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -7,14 +7,19 @@
 
 
 def process_data(
-    df, has_investments, labels, update_info, anchoring_info, purpose="estimation"
+    df,
+    has_endogenous_factors,
+    labels,
+    update_info,
+    anchoring_info,
+    purpose="estimation",
 ):
     """Process the data for estimation.
 
     Args:
         df (DataFrame): panel dataset in long format. It has a MultiIndex
             where the first level indicates the period and the second the individual.
-        has_investments (bool):
+        has_endogenous_factors (bool):
         labels (dict): Dict of lists with labels for the model quantities like
             factors, periods, controls, stagemap and stages. See :ref:`labels`
         update_info (pandas.DataFrame): DataFrame with one row per Kalman update needed
@@ -38,8 +43,8 @@ def process_data(
     df["constant"] = 1
     out = {}
 
-    if has_investments:
-        df = _augment_data_for_investments(df, labels, update_info)
+    if has_endogenous_factors:
+        df = _augment_data_for_endogenous_factors(df, labels, update_info)
     else:
         df = _add_copies_of_anchoring_outcome(df, anchoring_info)
     _check_data(df, update_info, labels, purpose=purpose)
@@ -85,7 +90,7 @@ def pre_process_data(df, periods):
     return df
 
 
-def _get_period_data_for_investments(
+def _get_period_data_for_endogenous_factors(
     aug_period: int,
     period: int,
     df: pd.DataFrame,
@@ -111,14 +116,16 @@ def _get_period_data_for_investments(
     return out
 
 
-def _augment_data_for_investments(
+def _augment_data_for_endogenous_factors(
     df: pd.DataFrame,
     labels: dict[str, Any],
     update_info: pd.DataFrame,
 ):
-    """Make room for endogenous investments by doubling up the periods.
+    """Make room for endogenous factors by doubling up the periods.
 
-    Endogeneity of investments means that current states influence the
+    Endogeneity means that current states influence the factor. Typically, this comes
+    as an investment equation. We make that look like a transition for skillmodels'
+    internal machinery.
 
     """
     df = df.reset_index()
@@ -130,7 +137,7 @@ def _augment_data_for_investments(
 
     out = pd.concat(
         [
-            _get_period_data_for_investments(
+            _get_period_data_for_endogenous_factors(
                 aug_period=aug_period,
                 period=period,
                 df=df,
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index cf67825..6183f0c 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -40,33 +40,37 @@ def process_model(model_dict):
           loadings and intercepts for each factor. See :ref:`normalizations`.
 
     """
-    has_investments = get_has_investments(model_dict["factors"])
-    dims = get_dimensions(model_dict=model_dict, has_investments=has_investments)
+    has_endogenous_factors = get_has_endogenous_factors(model_dict["factors"])
+    dims = get_dimensions(
+        model_dict=model_dict, has_endogenous_factors=has_endogenous_factors
+    )
     labels = _get_labels(
-        model_dict=model_dict, has_investments=has_investments, dimensions=dims
+        model_dict=model_dict,
+        has_endogenous_factors=has_endogenous_factors,
+        dimensions=dims,
     )
-    anchoring = _process_anchoring(model_dict, has_investments)
-    if has_investments:
-        _model_dict_aug = _augment_periods_for_investments(
+    anchoring = _process_anchoring(model_dict, has_endogenous_factors)
+    if has_endogenous_factors:
+        _model_dict_aug = _augment_periods_for_endogenous_factors(
             model_dict=model_dict,
             dimensions=dims,
             labels=labels,
         )
-        investments_info = _get_investments_info(
-            has_investments=has_investments,
+        endogenous_factors_info = _get_endogenous_factors_info(
+            has_endogenous_factors=has_endogenous_factors,
             model_dict=_model_dict_aug,
             labels=labels,
             bounds_distance=model_dict["estimation_options"]["bounds_distance"],
         )
     else:
         _model_dict_aug = model_dict
-        investments_info = {"has_investments": has_investments}
+        endogenous_factors_info = {"has_endogenous_factors": has_endogenous_factors}
     check_model(
         model_dict=_model_dict_aug,
         labels=labels,
         dimensions=dims,
         anchoring=anchoring,
-        has_investments=has_investments,
+        has_endogenous_factors=has_endogenous_factors,
     )
     transition_info = _get_transition_info(_model_dict_aug, labels)
     labels["transition_names"] = list(transition_info["function_names"].values())
@@ -79,42 +83,44 @@ def process_model(model_dict):
         "transition_info": transition_info,
         "update_info": _get_update_info(_model_dict_aug, dims, labels, anchoring),
         "normalizations": _process_normalizations(_model_dict_aug, dims, labels),
-        "investments_info": investments_info,
+        "endogenous_factors_info": endogenous_factors_info,
     }
     return processed
 
 
-def get_has_investments(factors: dict[str, Any]) -> bool:
-    """Return True if any investment factors are present."""
-    investments = pd.DataFrame(
+def get_has_endogenous_factors(factors: dict[str, Any]) -> bool:
+    """Return True if any endogenous factors are present."""
+    endogenous_factors = pd.DataFrame(
         [
             {
                 "factor": f,
-                "is_investment": v.get("is_investment", False),
+                "is_endogenous": v.get("is_endogenous", False),
                 "is_correction": v.get("is_correction", False),
             }
             for f, v in factors.items()
         ]
     ).set_index("factor")
-    if (investments.dtypes != bool).any():  # noqa: E721
+    if (endogenous_factors.dtypes != bool).any():  # noqa: E721
         raise ValueError(
-            "If specified, 'is_investment' and 'is_correction' both need to be of type"
-            f"'bool', got:\n{investments}"
+            "If specified, 'is_endogenous' and 'is_correction' both need to be of type"
+            f"'bool', got:\n{endogenous_factors}"
         )
-    if (~investments["is_investment"] & investments["is_correction"]).any():
+    if (
+        ~endogenous_factors["is_endogenous"] & endogenous_factors["is_correction"]
+    ).any():
         raise ValueError(
-            "A factor cannot be a correction and not an investment, got:\n"
-            f"{investments}"
+            "A factor cannot be a correction and not endogenous, got:\n"
+            f"{endogenous_factors}"
         )
-    return investments["is_investment"].any()
+    return endogenous_factors["is_endogenous"].any()
 
 
-def get_dimensions(model_dict, has_investments):
+def get_dimensions(model_dict, has_endogenous_factors):
     """Extract the dimensions of the model.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
-        has_investments (bool): Whether investment factors are present.
+        has_endogenous_factors (bool): Whether endogenous factors are present.
 
     Returns:
         dict: Dimensional information like n_states, n_periods, n_controls,
@@ -123,7 +129,7 @@ def get_dimensions(model_dict, has_investments):
     """
     all_n_periods = [len(d["measurements"]) for d in model_dict["factors"].values()]
     n_periods = max(all_n_periods)
-    n_aug_periods = 2 * n_periods if has_investments else n_periods
+    n_aug_periods = 2 * n_periods if has_endogenous_factors else n_periods
 
     dims = {
         "n_latent_factors": len(model_dict["factors"]),
@@ -138,23 +144,23 @@ def get_dimensions(model_dict, has_investments):
 
 
 def _get_aug_periods_to_periods(
-    n_aug_periods: int, has_investments: bool
+    n_aug_periods: int, has_endogenous_factors: bool
 ) -> dict[int, int]:
     """Return mapper of (potentially) augmented periods to user-provided periods."""
     aug_periods = list(range(n_aug_periods))
     return (
         {p: p // 2 for p in aug_periods}
-        if has_investments
+        if has_endogenous_factors
         else {p: p for p in aug_periods}
     )
 
 
-def _get_labels(model_dict, has_investments, dimensions):
+def _get_labels(model_dict, has_endogenous_factors, dimensions):
     """Extract labels of the model quantities.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
-        has_investments (bool): Whether investment factors are present.
+        has_endogenous_factors (bool): Whether endogenous factors are present.
         dimensions (dict): Dimensional information like n_states, n_periods, n_controls,
             n_mixtures. See :ref:`dimensions`.
 
@@ -165,7 +171,7 @@ def _get_labels(model_dict, has_investments, dimensions):
     """
     aug_periods_to_periods = _get_aug_periods_to_periods(
         n_aug_periods=dimensions["n_aug_periods"],
-        has_investments=has_investments,
+        has_endogenous_factors=has_endogenous_factors,
     )
 
     stagemap = model_dict.get("stagemap", list(range(dimensions["n_periods"] - 1)))
@@ -179,7 +185,7 @@ def _get_labels(model_dict, has_investments, dimensions):
     )
     if report:
         raise ValueError(f"Invalid stage map: {report}")
-    if has_investments:
+    if has_endogenous_factors:
         aug_stagemap = []
         aug_stages_to_stages = {}
         relevant_aug_periods = sorted(aug_periods_to_periods.keys())[:-2]
@@ -239,19 +245,21 @@ def _process_estimation_options(model_dict):
     return default_options
 
 
-def _process_anchoring(model_dict, has_investments):
+def _process_anchoring(model_dict, has_endogenous_factors):
     """Process the specification that governs how latent factors are anchored.
 
     Args:
         model_dict (dict): The model specification. See: :ref:`model_specs`
-        has_investments (bool): Whether the model has any investments.
+        has_endogenous_factors (bool): Whether the model has any endogenous factors.
 
     Returns:
         dict: Dictionary with information about anchoring. See :ref:`anchoring`
 
     """
-    if "anchoring" in model_dict and has_investments:
-        raise ValueError("anchoring is not supported when investments are present.")
+    if "anchoring" in model_dict and has_endogenous_factors:
+        raise ValueError(
+            "anchoring is not supported when endogenous factors are present."
+        )
 
     anchinfo = {
         "anchoring": False,
@@ -278,10 +286,10 @@ def _insert_empty_elements_into_list(old, insert_at_modulo, to_insert, aug_p_to_
     ]
 
 
-def _augment_periods_for_investments(
+def _augment_periods_for_endogenous_factors(
     model_dict: dict[str, Any], dimensions: dict[str, Any], labels: dict[str, Any]
 ) -> dict[str, Any]:
-    """Insert periods without measurements / normalisations if investments are present.
+    """Insert periods without measurements / normalisations if endogenous factors are present.
 
     Args:
         model_dict: The model specification. See: :ref:`model_specs`
@@ -296,7 +304,7 @@ def _augment_periods_for_investments(
     """
     aug = deepcopy(model_dict)
     for fac, v in model_dict["factors"].items():
-        insert_at_modulo = 0 if v.get("is_investment", False) else 1
+        insert_at_modulo = 0 if v.get("is_endogenous", False) else 1
 
         # Insert empty elements into measurements when we do not have those.
         if len(v["measurements"]) != dimensions["n_periods"]:
@@ -397,38 +405,38 @@ def _extract_factor(states, pos):
     return out
 
 
-def _get_investments_info(
-    has_investments: bool,
+def _get_endogenous_factors_info(
+    has_endogenous_factors: bool,
     model_dict: dict[str, Any],
     labels: dict[str, Any],
     bounds_distance: float,
 ) -> dict[str, Any]:
-    """Collect information about investments."""
-    investments_info = {
-        "has_investments": has_investments,
+    """Collect information about endogenous factors."""
+    endogenous_factors_info = {
+        "has_endogenous_factors": has_endogenous_factors,
         "aug_periods_to_aug_period_types": _get_aug_periods_to_aug_period_types(
             aug_periods=labels["aug_periods_to_periods"].keys(),
-            has_investments=has_investments,
+            has_endogenous_factors=has_endogenous_factors,
         ),
         "bounds_distance": bounds_distance,
     }
     for fac, v in model_dict["factors"].items():
-        investments_info[fac] = {
+        endogenous_factors_info[fac] = {
             "is_state": (
-                not v.get("is_investment", False) and not v.get("is_correction", False)
+                not v.get("is_endogenous", False) and not v.get("is_correction", False)
             ),
-            "is_investment": v.get("is_investment", False),
+            "is_endogenous": v.get("is_endogenous", False),
             "is_correction": v.get("is_correction", False),
         }
-    return investments_info
+    return endogenous_factors_info
 
 
 def _get_aug_periods_to_aug_period_types(
-    aug_periods: list[int], has_investments: bool
-) -> dict[int, Literal["states", "investments"]]:
+    aug_periods: list[int], has_endogenous_factors: bool
+) -> dict[int, Literal["states", "endogenous_factors"]]:
     return {
-        aug_p: ("states" if aug_p % 2 == 0 else "investments")
-        if has_investments
+        aug_p: ("states" if aug_p % 2 == 0 else "endogenous_factors")
+        if has_endogenous_factors
         else {aug_p: "states"}
         for aug_p in aug_periods
     }
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index 3cdb41e..32b7f87 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -52,7 +52,9 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
     if data is not None:
         processed_data = process_data(
             df=data,
-            has_investments=model["investments_info"]["has_investments"],
+            has_endogenous_factors=model["endogenous_factors_info"][
+                "has_endogenous_factors"
+            ],
             labels=model["labels"],
             update_info=model["update_info"],
             anchoring_info=model["anchoring"],
@@ -79,7 +81,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         labels=model["labels"],
         dimensions=model["dimensions"],
         transition_info=model["transition_info"],
-        investments_info=model["investments_info"],
+        endogenous_factors_info=model["endogenous_factors_info"],
     )
 
     params = params.reindex(params_index)
@@ -89,7 +91,9 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
-        has_investments=model["investments_info"]["has_investments"],
+        has_endogenous_factors=model["endogenous_factors_info"][
+            "has_endogenous_factors"
+        ],
     )
 
     states, covs, log_weights, pardict = parse_params(
@@ -108,7 +112,9 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         labels=model["labels"],
         dimensions=model["dimensions"],
         n_obs=n_obs,
-        has_investments=model["investments_info"]["has_investments"],
+        has_endogenous_factors=model["endogenous_factors_info"][
+            "has_endogenous_factors"
+        ],
         update_info=model["update_info"],
         control_data=control_data,
         observed_factor_data=observed_factor_data,
@@ -151,7 +157,7 @@ def _simulate_dataset(
     labels,
     dimensions,
     n_obs,
-    has_investments,
+    has_endogenous_factors,
     update_info,
     control_data,
     observed_factor_data,
@@ -170,7 +176,7 @@ def _simulate_dataset(
     policies = policies if policies is not None else []
 
     n_states = dimensions["n_latent_factors"]
-    if has_investments:
+    if has_endogenous_factors:
         n_periods = dimensions["n_aug_periods"] - 2
     else:
         n_periods = dimensions["n_aug_periods"]
diff --git a/src/skillmodels/utilities.py b/src/skillmodels/utilities.py
index 154c910..dd4f385 100644
--- a/src/skillmodels/utilities.py
+++ b/src/skillmodels/utilities.py
@@ -5,7 +5,11 @@
 import pandas as pd
 
 from skillmodels.params_index import get_params_index
-from skillmodels.process_model import get_dimensions, get_has_investments, process_model
+from skillmodels.process_model import (
+    get_dimensions,
+    get_has_endogenous_factors,
+    process_model,
+)
 
 
 def extract_factors(factors, model_dict, params=None):
@@ -81,8 +85,8 @@ def remove_factors(factors, model_dict, params=None):
         pandas.DataFrame: The reduced parameter DataFrame (only if params is not None)
 
     """
-    # We need this for the full model when investments are present.
-    has_investments = get_has_investments(model_dict["factors"])
+    # We need this for the full model when endogenous factors are present.
+    has_endogenous_factors = get_has_endogenous_factors(model_dict["factors"])
 
     out = deepcopy(model_dict)
 
@@ -97,14 +101,14 @@ def remove_factors(factors, model_dict, params=None):
         if out["anchoring"]["outcomes"] == {}:
             out = _remove_from_dict(out, "anchoring")
 
-    # Remove periods if necessary, but only if no investments are present.
+    # Remove periods if necessary, but only if no endogenous factors are present.
     # (else we would mess up the mapping between raw periods model periods)
-    if not has_investments:
-        new_n_periods = get_dimensions(out, has_investments)["n_periods"]
+    if not has_endogenous_factors:
+        new_n_periods = get_dimensions(out, has_endogenous_factors)["n_periods"]
         out = reduce_n_periods(out, new_n_periods)
 
     if params is not None:
-        out_params = _reduce_params(params, out, has_investments)
+        out_params = _reduce_params(params, out, has_endogenous_factors)
         out = (out, out_params)
 
     return out
@@ -150,8 +154,8 @@ def remove_measurements(measurements, model_dict, params=None):
             )
 
     if params is not None:
-        # This likely won't work if we have investments.
-        out_params = _reduce_params(params, out, has_investments=False)
+        # This likely won't work if we have endogenous factors.
+        out_params = _reduce_params(params, out, has_endogenous_factors=False)
         out = (out, out_params)
 
     return out
@@ -178,8 +182,8 @@ def remove_controls(controls, model_dict, params=None):
         out = _remove_from_dict(out, "controls")
 
     if params is not None:
-        # This likely won't work if we have investments.
-        out_params = _reduce_params(params, out, has_investments=False)
+        # This likely won't work if we have endogenous factors.
+        out_params = _reduce_params(params, out, has_endogenous_factors=False)
         out = (out, out_params)
 
     return out
@@ -205,8 +209,8 @@ def switch_translog_to_linear(model_dict, params=None):
             out["factors"][factor]["transition_function"] = "linear"
 
     if params is not None:
-        # This likely won't work if we have investments.
-        out_params = _reduce_params(params, out, has_investments=False)
+        # This likely won't work if we have endogenous factors.
+        out_params = _reduce_params(params, out, has_endogenous_factors=False)
         out = (out, out_params)
 
     return out
@@ -294,7 +298,7 @@ def _remove_from_dict(dict_, to_remove):
     return {key: val for key, val in dict_.items() if key not in to_remove}
 
 
-def _reduce_params(params, model_dict, has_investments):
+def _reduce_params(params, model_dict, has_endogenous_factors):
     """Reduce a parameter DataFrame from a larger model to a reduced model.
 
     The reduced model must be nested in the original model for which the params
@@ -303,15 +307,15 @@ def _reduce_params(params, model_dict, has_investments):
     Args:
         params (pandas.DataFrame or None): The params DataFrame for the full model.
         model_dict (dict): The model specification. See: :ref:`model_specs`.
-        has_investments (bool): Whether the model has investments.
+        has_endogenous_factors (bool): Whether the model has endogenous factors.
 
     Returns:
         pandas.DataFrame: The reduced parameters DataFrame.
 
     """
     index = _get_params_index_from_model_dict(model_dict)
-    # If we have investments, we need to keep the periods from params.
-    if has_investments:
+    # If we have endogenous factors, we need to keep the periods from params.
+    if has_endogenous_factors:
         df = pd.merge(
             left=params.reset_index(),
             right=index.to_frame(index=False)[
@@ -344,7 +348,7 @@ def _get_params_index_from_model_dict(model_dict):
         labels=mod["labels"],
         dimensions=mod["dimensions"],
         transition_info=mod["transition_info"],
-        investments_info=mod["investments_info"],
+        endogenous_factors_info=mod["endogenous_factors_info"],
     )
     return index
 
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 9fdc0e3..e630b28 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -343,7 +343,9 @@ def _get_pardict(model, params):
         update_info=model["update_info"],
         labels=model["labels"],
         anchoring=model["anchoring"],
-        has_investments=model["investments_info"]["has_investments"],
+        has_endogenous_factors=model["endogenous_factors_info"][
+            "has_endogenous_factors"
+        ],
     )
 
     _, _, _, pardict = parse_params(
@@ -363,7 +365,7 @@ def _set_index_params(model, params):
         labels=model["labels"],
         dimensions=model["dimensions"],
         transition_info=model["transition_info"],
-        investments_info=model["investments_info"],
+        endogenous_factors_info=model["endogenous_factors_info"],
     )
 
     params = params.reindex(params_index)
@@ -380,14 +382,16 @@ def _get_states_data(model, period, data, states, observed_factors):
     if observed_factors:
         _observed_arr = process_data(
             df=data,
-            has_investments=model["investments_info"]["has_investments"],
+            has_endogenous_factors=model["endogenous_factors_info"][
+                "has_endogenous_factors"
+            ],
             labels=model["labels"],
             update_info=model["update_info"],
             anchoring_info=model["anchoring"],
         )["observed_factors"]
         # convert from jax to numpy
         _observed_arr = np.array(_observed_arr)
-        if model["investments_info"]["has_investments"]:
+        if model["endogenous_factors_info"]["has_endogenous_factors"]:
             both_aug_periods = [
                 aug_p
                 for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
@@ -481,7 +485,7 @@ def _prepare_data_for_one_plot_average_2d(
     transition_params,
     all_factors,
 ):
-    if model["investments_info"]["has_investments"]:
+    if model["endogenous_factors_info"]["has_endogenous_factors"]:
         aug_periods = [
             aug_p
             for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
diff --git a/tests/model2_with_inv_correct_update_info.csv b/tests/model2_with_endog_correct_update_info.csv
similarity index 100%
rename from tests/model2_with_inv_correct_update_info.csv
rename to tests/model2_with_endog_correct_update_info.csv
diff --git a/tests/simplest_augmented_model.yaml b/tests/simplest_augmented_model.yaml
index e48aa25..04e892b 100644
--- a/tests/simplest_augmented_model.yaml
+++ b/tests/simplest_augmented_model.yaml
@@ -2,7 +2,7 @@
 factors:
   fac1:
     is_correction: false
-    is_investment: false
+    is_endogenous: false
     measurements:
       -   - var
       -   - var
@@ -13,7 +13,7 @@ factors:
     transition_function: linear
   fac2:
     is_correction: false
-    is_investment: true
+    is_endogenous: true
     measurements:
       -   - inv
       -   - inv
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index 9e2714e..9d5e449 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -138,7 +138,7 @@ def test_stage_constraints():
     assert_list_equal_except_for_order(calculated, expected)
 
 
-def test_stage_constraints_with_investments():
+def test_stage_constraints_with_endogenous_factors():
     stages = [0, 1, 2, 3]
     stagemap = [0, 1, 0, 1, 2, 3]
     expected = [
@@ -379,7 +379,7 @@ def simplest_augmented_model():
 def test_get_constraints_for_augmented_periods(simplest_augmented_model):
     calculated = _get_constraints_for_augmented_periods(
         labels=simplest_augmented_model["labels"],
-        investments_info=simplest_augmented_model["investments_info"],
+        endogenous_factors_info=simplest_augmented_model["endogenous_factors_info"],
     )
     for c in calculated:
         del c["description"]
diff --git a/tests/test_params_index.py b/tests/test_params_index.py
index 683ada3..315f2d0 100644
--- a/tests/test_params_index.py
+++ b/tests/test_params_index.py
@@ -30,7 +30,7 @@ def model2_inputs():
         "labels": processed["labels"],
         "dimensions": processed["dimensions"],
         "transition_info": processed["transition_info"],
-        "investments_info": processed["investments_info"],
+        "endogenous_factors_info": processed["endogenous_factors_info"],
     }
     return out
 
@@ -174,7 +174,7 @@ def test_initial_cov_index_tuples():
     assert calculated == expected
 
 
-def test_trans_coeffs_index_tuples_no_investments():
+def test_trans_coeffs_index_tuples_no_endogenous_factors():
     periods = [0, 1, 2]
 
     param_names = {
@@ -206,13 +206,13 @@ def test_trans_coeffs_index_tuples_no_investments():
     calculated = get_transition_index_tuples(
         transition_info=trans_info,
         periods=periods,
-        has_investments=False,
+        has_endogenous_factors=False,
     )
 
     assert calculated == expected
 
 
-def test_trans_coeffs_index_tuples_has_investments():
+def test_trans_coeffs_index_tuples_has_endogenous_factors():
     periods = [0, 1, 2, 3, 4, 5]
 
     param_names = {
@@ -260,7 +260,7 @@ def test_trans_coeffs_index_tuples_has_investments():
     calculated = get_transition_index_tuples(
         transition_info=trans_info,
         periods=periods,
-        has_investments=True,
+        has_endogenous_factors=True,
     )
 
     assert calculated == expected
diff --git a/tests/test_parse_params.py b/tests/test_parse_params.py
index dffaf7f..33ffbe8 100644
--- a/tests/test_parse_params.py
+++ b/tests/test_parse_params.py
@@ -43,7 +43,7 @@ def parsed_parameters():
         update_info=update_info,
         labels=labels,
         anchoring=anchoring,
-        has_investments=False,
+        has_endogenous_factors=False,
     )
 
     params_vec = jnp.arange(len(p_index))
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index 502a91f..36a40e8 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -10,7 +10,7 @@
 from numpy.testing import assert_array_equal as aae
 
 from skillmodels.process_data import (
-    _augment_data_for_investments,
+    _augment_data_for_endogenous_factors,
     _generate_controls_array,
     _generate_measurements_array,
     _generate_observed_factor_array,
@@ -60,13 +60,13 @@ def simplest_augmented():
     return out
 
 
-def test_augment_data_for_investments(simplest_augmented):
+def test_augment_data_for_endogenous_factors(simplest_augmented):
     model = process_model(simplest_augmented["model_dict"])
     pre_processed_data = pre_process_data(
         simplest_augmented["data_input"], model["labels"]["periods"]
     )
     pre_processed_data["constant"] = 1
-    res = _augment_data_for_investments(
+    res = _augment_data_for_endogenous_factors(
         df=pre_processed_data,
         labels=model["labels"],
         update_info=model["update_info"],
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index 6cbad11..ace15dc 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -6,7 +6,7 @@
 import yaml
 from pandas.testing import assert_frame_equal
 
-from skillmodels.process_model import get_has_investments, process_model
+from skillmodels.process_model import get_has_endogenous_factors, process_model
 
 # ======================================================================================
 # Integration test with model2 from the replication files of CHS2010
@@ -23,8 +23,11 @@ def model2():
     return model_dict
 
 
-def test_has_investments(model2):
-    assert process_model(model2)["investments_info"]["has_investments"] == False
+def test_has_endogenous_factors(model2):
+    assert (
+        process_model(model2)["endogenous_factors_info"]["has_endogenous_factors"]
+        == False
+    )
 
 
 def test_dimensions(model2):
@@ -122,38 +125,39 @@ def test_normalizations(model2):
 
 
 # ======================================================================================
-# Augment model2 with investments
+# Augment model2 with endogenous factors
 # ======================================================================================
 
 
-def test_anchoring_and_investments_conflict():
+def test_anchoring_and_endogenous_factors_conflict():
     with open(TEST_DIR / "model2.yaml") as y:
         model_dict = yaml.load(y, Loader=yaml.FullLoader)
-    # Set fac3 to be an investment
-    model_dict["factors"]["fac3"]["is_investment"] = True
+    # Set fac3 to be endogenous
+    model_dict["factors"]["fac3"]["is_endogenous"] = True
     del model_dict["stagemap"]
     with pytest.raises(
-        ValueError, match=r"anchoring is not supported when investments are present."
+        ValueError,
+        match=r"anchoring is not supported when endogenous factors are present.",
     ):
         process_model(model_dict)
 
 
-def test_stagemap_with_investments_wrong_labels():
+def test_stagemap_with_endogenous_factors_wrong_labels():
     with open(TEST_DIR / "model2.yaml") as y:
         model_dict = yaml.load(y, Loader=yaml.FullLoader)
-    # Set fac3 to be an investment
-    model_dict["factors"]["fac3"]["is_investment"] = True
+    # Set fac3 to be endogenous
+    model_dict["factors"]["fac3"]["is_endogenous"] = True
     model_dict["stagemap"] = [0, 0, 1, 1, 2, 2, 4]
     del model_dict["anchoring"]
     with pytest.raises(ValueError, match="Invalid stage map:"):
         process_model(model_dict)
 
 
-def test_stagemap_with_investments():
+def test_stagemap_with_endogenous_factors():
     with open(TEST_DIR / "model2.yaml") as y:
         model_dict = yaml.load(y, Loader=yaml.FullLoader)
-    # Set fac3 to be an investment
-    model_dict["factors"]["fac3"]["is_investment"] = True
+    # Set fac3 to be endogenous
+    model_dict["factors"]["fac3"]["is_endogenous"] = True
     model_dict["stagemap"] = [0, 0, 1, 1, 2, 2, 3]
     del model_dict["anchoring"]
     model = process_model(model_dict)
@@ -166,18 +170,21 @@ def test_stagemap_with_investments():
 def model2_inv():
     with open(TEST_DIR / "model2.yaml") as y:
         model_dict = yaml.load(y, Loader=yaml.FullLoader)
-    # Set fac3 to be an investment
-    model_dict["factors"]["fac3"]["is_investment"] = True
+    # Set fac3 to be endogenous
+    model_dict["factors"]["fac3"]["is_endogenous"] = True
     del model_dict["stagemap"]
     del model_dict["anchoring"]
     return model_dict
 
 
-def test_with_inv_has_investments(model2_inv):
-    assert process_model(model2_inv)["investments_info"]["has_investments"] == True
+def test_with_endog_has_endogenous_factors(model2_inv):
+    assert (
+        process_model(model2_inv)["endogenous_factors_info"]["has_endogenous_factors"]
+        == True
+    )
 
 
-def test_with_inv_dimensions(model2_inv):
+def test_with_endog_dimensions(model2_inv):
     res = process_model(model2_inv)["dimensions"]
     assert res["n_latent_factors"] == 3
     assert res["n_observed_factors"] == 0
@@ -188,7 +195,7 @@ def test_with_inv_dimensions(model2_inv):
     assert res["n_mixtures"] == 1
 
 
-def test_with_inv_labels(model2_inv):
+def test_with_endog_labels(model2_inv):
     res = process_model(model2_inv)["labels"]
     n_aug_periods = 16
     assert res["latent_factors"] == ["fac1", "fac2", "fac3"]
@@ -201,14 +208,14 @@ def test_with_inv_labels(model2_inv):
     assert res["aug_stages"] == list(range(n_aug_periods - 2))
 
 
-def test_with_inv_estimation_options(model2_inv):
+def test_with_endog_estimation_options(model2_inv):
     res = process_model(model2_inv)["estimation_options"]
     assert res["sigma_points_scale"] == 2
     assert res["robust_bounds"]
     assert res["bounds_distance"] == 0.001
 
 
-def test_with_inv_anchoring_is_empty(model2_inv):
+def test_with_endog_anchoring_is_empty(model2_inv):
     res = process_model(model2_inv)["anchoring"]
     assert res["outcomes"] == {}
     assert res["factors"] == []
@@ -217,7 +224,7 @@ def test_with_inv_anchoring_is_empty(model2_inv):
     assert res["free_loadings"] is False
 
 
-def test_with_inv_transition_info(model2_inv):
+def test_with_endog_transition_info(model2_inv):
     res = process_model(model2_inv)["transition_info"]
 
     assert isinstance(res, dict)
@@ -226,17 +233,17 @@ def test_with_inv_transition_info(model2_inv):
     assert list(inspect.signature(res["func"]).parameters) == ["params", "states"]
 
 
-def test_with_inv_update_info(model2_inv):
+def test_with_endog_update_info(model2_inv):
     res = process_model(model2_inv)["update_info"]
     test_dir = Path(__file__).parent.resolve()
     expected = pd.read_csv(
-        test_dir / "model2_with_inv_correct_update_info.csv",
+        test_dir / "model2_with_endog_correct_update_info.csv",
         index_col=["period", "variable"],
     )
     assert_frame_equal(res, expected)
 
 
-def test_with_inv_normalizations(model2_inv):
+def test_with_endog_normalizations(model2_inv):
     expected = {
         "fac1": {
             "loadings": [
@@ -363,35 +370,35 @@ def test_with_inv_normalizations(model2_inv):
 # ======================================================================================
 
 
-def test_model_has_investments_not_specified():
+def test_model_has_endogenous_factors_not_specified():
     factors = {"a": {}}
-    assert get_has_investments(factors) == False
+    assert get_has_endogenous_factors(factors) == False
 
 
-def test_get_has_investments_wrong_type():
-    factors = {"a": {"is_investment": 3}}
+def test_get_has_endogenous_factors_wrong_type():
+    factors = {"a": {"is_endogenous": 3}}
     with pytest.raises(ValueError):
-        get_has_investments(factors)
+        get_has_endogenous_factors(factors)
 
 
-def test_get_has_investments_wrong_constellation():
-    factors = {"a": {"is_investment": False, "is_correction": True}}
+def test_get_has_endogenous_factors_wrong_constellation():
+    factors = {"a": {"is_endogenous": False, "is_correction": True}}
     with pytest.raises(ValueError):
-        get_has_investments(factors)
+        get_has_endogenous_factors(factors)
 
 
-def test_get_has_investments_indeed():
+def test_get_has_endogenous_factors_indeed():
     factors = {
-        "a": {"is_investment": True, "is_correction": False},
-        "b": {"is_investment": False, "is_correction": False},
+        "a": {"is_endogenous": True, "is_correction": False},
+        "b": {"is_endogenous": False, "is_correction": False},
     }
-    assert get_has_investments(factors) == True
+    assert get_has_endogenous_factors(factors) == True
 
 
-def test_get_has_investments_and_correction():
+def test_get_has_endogenous_factors_and_correction():
     factors = {
-        "a": {"is_investment": True, "is_correction": False},
-        "b": {"is_investment": False, "is_correction": False},
-        "c": {"is_investment": True, "is_correction": True},
+        "a": {"is_endogenous": True, "is_correction": False},
+        "b": {"is_endogenous": False, "is_correction": False},
+        "c": {"is_endogenous": True, "is_correction": True},
     }
-    assert get_has_investments(factors) == True
+    assert get_has_endogenous_factors(factors) == True

From 97027d06133e01913080c33caf945342556f908b Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Mon, 6 Oct 2025 13:44:43 +0200
Subject: [PATCH 38/48] Get rid of deprecation warning.

---
 src/skillmodels/qr.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/skillmodels/qr.py b/src/skillmodels/qr.py
index 1cf835d..c690eac 100644
--- a/src/skillmodels/qr.py
+++ b/src/skillmodels/qr.py
@@ -47,9 +47,7 @@ def _tril(m: jax.Array, k: int = 0) -> jax.Array:
     """Select lower Triangle of a Matrix."""
     *_, dim_n, dim_m = m.shape
     mask = jnp.tri(dim_n, dim_m, k, bool)
-    return jax.lax.select(
-        jax.lax.broadcast(mask, m.shape[:-2]), m, jax.lax.zeros_like_array(m)
-    )
+    return jax.lax.select(jax.lax.broadcast(mask, m.shape[:-2]), m, jnp.zeros_like(m))
 
 
 @qr_gpu.defjvp
@@ -59,12 +57,12 @@ def qr_jvp_rule(primals, tangents):
     (x,) = primals
     (dx,) = tangents
     q, r = qr_gpu(x)
-    *_, m, n = x.shape
     dx_rinv = jax.lax.linalg.triangular_solve(r, dx)  # Right side solve by default
     qt_dx_rinv = _h(q) @ dx_rinv
     qt_dx_rinv_lower = _tril(qt_dx_rinv, -1)
     do = qt_dx_rinv_lower - _h(qt_dx_rinv_lower)  # This is skew-symmetric
     # The following correction is necessary for complex inputs
+    n = x.shape[-1]
     i = jax.lax.expand_dims(jnp.eye(n, n), range(qt_dx_rinv.ndim - 2))
     do = do + i * (qt_dx_rinv - qt_dx_rinv.real.astype(qt_dx_rinv.dtype))
     dq = q @ (do - qt_dx_rinv) + dx_rinv

From 2b3963e2022dbea1edaa965f8949d4f012101b01 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 6 Oct 2025 14:13:40 +0200
Subject: [PATCH 39/48] Use 'aug_period' as the index.

---
 src/skillmodels/params_index.py | 34 ++++++++++++++++-----------------
 tests/test_params_index.py      |  4 ++--
 tests/test_utilities.py         |  4 ++--
 3 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/src/skillmodels/params_index.py b/src/skillmodels/params_index.py
index 1445f3c..9251d82 100644
--- a/src/skillmodels/params_index.py
+++ b/src/skillmodels/params_index.py
@@ -33,7 +33,7 @@ def get_params_index(
     )
     ind_tups += get_meas_sds_index_tuples(update_info=update_info)
     ind_tups += get_shock_sds_index_tuples(
-        periods=labels["aug_periods"],
+        aug_periods=labels["aug_periods"],
         factors=labels["latent_factors"],
         has_endogenous_factors=endogenous_factors_info["has_endogenous_factors"],
     )
@@ -48,13 +48,13 @@ def get_params_index(
     )
     ind_tups += get_transition_index_tuples(
         transition_info=transition_info,
-        periods=labels["aug_periods"],
+        aug_periods=labels["aug_periods"],
         has_endogenous_factors=endogenous_factors_info["has_endogenous_factors"],
     )
 
     index = pd.MultiIndex.from_tuples(
         ind_tups,
-        names=["category", "period", "name1", "name2"],
+        names=["category", "aug_period", "name1", "name2"],
     )
     return index
 
@@ -70,9 +70,9 @@ def get_control_params_index_tuples(controls, update_info):
 
     """
     ind_tups = []
-    for period, meas in update_info.index:
+    for aug_period, meas in update_info.index:
         for cont in controls:
-            ind_tups.append(("controls", period, meas, cont))
+            ind_tups.append(("controls", aug_period, meas, cont))
     return ind_tups
 
 
@@ -90,10 +90,10 @@ def get_loadings_index_tuples(factors, update_info):
     """
     mask = update_info[factors].to_numpy()
     ind_tups = []
-    for i, (period, meas) in enumerate(update_info.index):
+    for i, (aug_period, meas) in enumerate(update_info.index):
         for f, factor in enumerate(factors):
             if mask[i, f]:
-                ind_tups.append(("loadings", period, meas, factor))
+                ind_tups.append(("loadings", aug_period, meas, factor))
     return ind_tups
 
 
@@ -109,16 +109,16 @@ def get_meas_sds_index_tuples(update_info):
 
     """
     ind_tups = []
-    for period, meas in update_info.index:
-        ind_tups.append(("meas_sds", period, meas, "-"))
+    for aug_period, meas in update_info.index:
+        ind_tups.append(("meas_sds", aug_period, meas, "-"))
     return ind_tups
 
 
-def get_shock_sds_index_tuples(periods, factors, has_endogenous_factors):
+def get_shock_sds_index_tuples(aug_periods, factors, has_endogenous_factors):
     """Index tuples for shock_sd.
 
     Args:
-        periods (list): The periods of the model.
+        aug_periods (list): The augmented periods of the model.
         factors (list): The latent factors of the model.
 
     Returns:
@@ -127,9 +127,9 @@ def get_shock_sds_index_tuples(periods, factors, has_endogenous_factors):
     """
     end = -2 if has_endogenous_factors else -1
     ind_tups = []
-    for period in periods[:end]:
+    for aug_period in aug_periods[:end]:
         for factor in factors:
-            ind_tups.append(("shock_sds", period, factor, "-"))
+            ind_tups.append(("shock_sds", aug_period, factor, "-"))
     return ind_tups
 
 
@@ -194,13 +194,13 @@ def get_initial_cholcovs_index_tuples(n_mixtures, factors):
     return ind_tups
 
 
-def get_transition_index_tuples(transition_info, periods, has_endogenous_factors):
+def get_transition_index_tuples(transition_info, aug_periods, has_endogenous_factors):
     """Index tuples for transition equation coefficients.
 
     Args:
         latent_factors (list): The latent factors of the model
         all_factors (list): The latent and observed factors of the model.
-        periods (list): The periods of the model
+        aug_periods (list): The augmented periods of the model
         transition_names (list): name of the transition equation of each factor
         has_endogenous_factors (bool): Whether the model has endogenous factors.
 
@@ -211,7 +211,7 @@ def get_transition_index_tuples(transition_info, periods, has_endogenous_factors
     end = -2 if has_endogenous_factors else -1
     ind_tups = []
     for factor, names in transition_info["param_names"].items():
-        for period in periods[:end]:
+        for aug_period in aug_periods[:end]:
             for name in names:
-                ind_tups.append(("transition", period, factor, name))
+                ind_tups.append(("transition", aug_period, factor, name))
     return ind_tups
diff --git a/tests/test_params_index.py b/tests/test_params_index.py
index 315f2d0..9e10d61 100644
--- a/tests/test_params_index.py
+++ b/tests/test_params_index.py
@@ -205,7 +205,7 @@ def test_trans_coeffs_index_tuples_no_endogenous_factors():
 
     calculated = get_transition_index_tuples(
         transition_info=trans_info,
-        periods=periods,
+        aug_periods=periods,
         has_endogenous_factors=False,
     )
 
@@ -259,7 +259,7 @@ def test_trans_coeffs_index_tuples_has_endogenous_factors():
 
     calculated = get_transition_index_tuples(
         transition_info=trans_info,
-        periods=periods,
+        aug_periods=periods,
         has_endogenous_factors=True,
     )
 
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index a35f700..8041fc9 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -152,7 +152,7 @@ def test_reduce_params_via_extract_factors(model2):
             ("mixture_weights", 0, "mixture_0", "-"),
             ("initial_cholcovs", 0, "mixture_0", "fac3-fac3"),
         ],
-        names=["category", "period", "name1", "name2"],
+        names=["category", "aug_period", "name1", "name2"],
     )
 
     assert_index_equal(reduced_params.index, expected_index)
@@ -176,7 +176,7 @@ def test_extend_params_via_switch_to_translog(model2):
             ("transition", 0, "fac2", "fac2 ** 2"),
             ("transition", 0, "fac2", "fac3 ** 2"),
         ],
-        names=["category", "period", "name1", "name2"],
+        names=["category", "aug_period", "name1", "name2"],
     )
 
     assert_index_equal(added_index, expected_added_index)

From 7a56efba0610d480112f12ec15859ee4a229e1b6 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 6 Oct 2025 14:38:31 +0200
Subject: [PATCH 40/48] More changes of periods -> aug_periods.

---
 src/skillmodels/constraints.py          |  4 +--
 src/skillmodels/maximization_inputs.py  |  6 ++---
 src/skillmodels/parse_params.py         |  2 +-
 src/skillmodels/process_data.py         | 18 ++++++-------
 src/skillmodels/process_debug_data.py   | 14 +++++-----
 src/skillmodels/transition_functions.py | 36 ++++++++++++++-----------
 6 files changed, 44 insertions(+), 36 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 6a96915..9829024 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -309,7 +309,7 @@ def _get_transition_constraints(labels) -> list[dict]:
             funcname = f"constraints_{tname}"
             if func := getattr(t_f_module, funcname, False):
                 c = func(
-                    factor=factor, factors=labels["all_factors"], period=aug_period
+                    factor=factor, factors=labels["all_factors"], aug_period=aug_period
                 )
                 if "description" not in c:
                     c["description"] = msg
@@ -422,7 +422,7 @@ def _get_constraints_for_augmented_periods(
             if func := getattr(t_f_module, f"identity_constraints_{tname}", False):
                 constraints_dicts += func(
                     factor=factor,
-                    period=aug_period,
+                    aug_period=aug_period,
                     all_factors=labels["all_factors"],
                 )
         for aug_period in aug_periods_to_constrain[:-1]:
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 574515f..5629eba 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -204,8 +204,8 @@ def _partial_some_log_likelihood(
 ):
     update_info = model["update_info"]
     is_measurement_iteration = (update_info["purpose"] == "measurement").to_numpy()
-    _periods = pd.Series(update_info.index.get_level_values("period").to_numpy())
-    is_predict_iteration = ((_periods - _periods.shift(-1)) == -1).to_numpy()
+    _aug_periods = pd.Series(update_info.index.get_level_values("period").to_numpy())
+    is_predict_iteration = ((_aug_periods - _aug_periods.shift(-1)) == -1).to_numpy()
     # iteration_to_period is used as an indexer to loop over arrays of different lengths
     # in a jax.lax.scan. It needs to work for arrays of length n_aug_periods and not raise
     # IndexErrors on tracer arrays of length n_aug_periods - 1 (i.e. n_transitions).
@@ -217,7 +217,7 @@ def _partial_some_log_likelihood(
         if parsing_info["has_endogenous_factors"]
         else model["labels"]["aug_periods"][-1]
     )
-    iteration_to_period = _periods.replace(last_aug_period, -1).to_numpy()
+    iteration_to_period = _aug_periods.replace(last_aug_period, -1).to_numpy()
     assert max(iteration_to_period) == last_aug_period - 1
 
     return functools.partial(
diff --git a/src/skillmodels/parse_params.py b/src/skillmodels/parse_params.py
index 7e2e819..427406c 100644
--- a/src/skillmodels/parse_params.py
+++ b/src/skillmodels/parse_params.py
@@ -11,7 +11,7 @@ def create_parsing_info(
     """Create a dictionary with information how the parameter vector has to be parsed.
 
     Args:
-        params_index (pandas.MultiIndex): It has the levels ["category", "period",
+        params_index (pandas.MultiIndex): It has the levels ["category", "aug_period",
             "name1", "name2"]
         update_info (pandas.DataFrame): DataFrame with one row per Kalman update needed
             in the likelihood function. See :ref:`update_info`.
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 87b5b75..a127eb8 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -194,12 +194,12 @@ def _check_data(df, update_info, labels, purpose):  # noqa: C901
 
 
 def _handle_controls_with_missings(df, controls, update_info):
-    periods = update_info.index.get_level_values(0).unique().tolist()
+    aug_periods = update_info.index.get_level_values(0).unique().tolist()
     problematic_index = df.index[:0]
-    for period in periods:
-        period_data = df.query(f"period == {period}")
+    for aug_period in aug_periods:
+        period_data = df.query(f"period == {aug_period}")
         control_data = period_data[controls]
-        meas_data = period_data[_get_period_measurements(update_info, period)]
+        meas_data = period_data[_get_period_measurements(update_info, aug_period)]
         problem = control_data.isna().any(axis=1) & meas_data.notna().any(axis=1)
         problematic_index = problematic_index.union(period_data[problem].index)
 
@@ -212,9 +212,9 @@ def _handle_controls_with_missings(df, controls, update_info):
     return df
 
 
-def _get_period_measurements(update_info, period):
-    if period in update_info.index:
-        measurements = list(update_info.loc[period].index)
+def _get_period_measurements(update_info, aug_period):
+    if aug_period in update_info.index:
+        measurements = list(update_info.loc[aug_period].index)
     else:
         measurements = []
     return measurements
@@ -222,8 +222,8 @@ def _get_period_measurements(update_info, period):
 
 def _generate_measurements_array(df, update_info, n_obs):
     arr = np.zeros((len(update_info), n_obs))
-    for k, (period, var) in enumerate(update_info.index):
-        arr[k] = df.query(f"period == {period}")[var].to_numpy()
+    for k, (aug_period, var) in enumerate(update_info.index):
+        arr[k] = df.query(f"period == {aug_period}")[var].to_numpy()
     return jnp.array(arr, dtype="float32")
 
 
diff --git a/src/skillmodels/process_debug_data.py b/src/skillmodels/process_debug_data.py
index 3ec3062..92eaa5b 100644
--- a/src/skillmodels/process_debug_data.py
+++ b/src/skillmodels/process_debug_data.py
@@ -95,9 +95,11 @@ def process_debug_data(debug_data, model):
 
 def _create_post_update_states(filtered_states, factors, update_info):
     to_concat = []
-    for (period, meas), data in zip(update_info.index, filtered_states, strict=False):
+    for (aug_period, meas), data in zip(
+        update_info.index, filtered_states, strict=False
+    ):
         df = _convert_state_array_to_df(data, factors)
-        df["period"] = period
+        df["period"] = aug_period
         df["id"] = np.arange(len(df))
         df["measurement"] = meas
         to_concat.append(df)
@@ -128,9 +130,9 @@ def _create_filtered_states(filtered_states, log_mixture_weights, update_info, f
     agg_states = (filtered_states * weights.reshape(*weights.shape, 1)).sum(axis=-2)
 
     keep = []
-    for i, (period, measurement) in enumerate(update_info.index):
+    for i, (aug_period, measurement) in enumerate(update_info.index):
         last_measurement = update_info.query(
-            f"purpose == 'measurement' & period == {period}",
+            f"purpose == 'measurement' & period == {aug_period}",
         ).index[-1][1]
 
         if measurement == last_measurement:
@@ -162,10 +164,10 @@ def create_state_ranges(filtered_states, factors):
 def _process_residuals(residuals, update_info):
     to_concat = []
     n_obs, n_mixtures = residuals[0].shape
-    for (period, meas), data in zip(update_info.index, residuals, strict=False):
+    for (aug_period, meas), data in zip(update_info.index, residuals, strict=False):
         df = pd.DataFrame(data.reshape(-1, 1), columns=["residual"])
         df["mixture"] = np.full((n_obs, n_mixtures), np.arange(n_mixtures)).flatten()
-        df["period"] = period
+        df["period"] = aug_period
         df["id"] = np.arange(len(df))
         df["measurement"] = meas
         to_concat.append(df)
diff --git a/src/skillmodels/transition_functions.py b/src/skillmodels/transition_functions.py
index 4a65a01..1642707 100644
--- a/src/skillmodels/transition_functions.py
+++ b/src/skillmodels/transition_functions.py
@@ -47,14 +47,14 @@ def params_linear(factors):
     return [*factors, "constant"]
 
 
-def identity_constraints_linear(factor, period, all_factors) -> list[dict]:
-    """Index tuples for linear transition function."""
+def identity_constraints_linear(factor, aug_period, all_factors) -> list[dict]:
+    """Identity constraints for linear transition function."""
     constraints_dicts = []
     for regressor in params_linear(all_factors):
         val = 1.0 if factor == regressor else 0.0
         constraints_dicts.append(
             {
-                "loc": ("transition", period, factor, regressor),
+                "loc": ("transition", aug_period, factor, regressor),
                 "type": "fixed",
                 "value": val,
                 "description": "Identity constraint.",
@@ -96,14 +96,14 @@ def params_translog(factors):
     return names
 
 
-def identity_constraints_translog(factor, period, all_factors) -> list[dict]:
-    """Index tuples for translog transition function."""
+def identity_constraints_translog(factor, aug_period, all_factors) -> list[dict]:
+    """Identity constraints for translog transition function."""
     constraints_dicts = []
     for regressor in params_translog(all_factors):
         val = 1.0 if factor == regressor else 0.0
         constraints_dicts.append(
             {
-                "loc": ("transition", period, factor, regressor),
+                "loc": ("transition", aug_period, factor, regressor),
                 "type": "fixed",
                 "value": val,
                 "description": "Identity constraint.",
@@ -133,13 +133,15 @@ def params_log_ces(factors):
     return [*factors, "phi"]
 
 
-def constraints_log_ces(factor, factors, period):
+def constraints_log_ces(factor, factors, aug_period):
+    """Constraints for log_ces production function."""
     names = params_log_ces(factors)
-    loc = [("transition", period, factor, name) for name in names[:-1]]
+    loc = [("transition", aug_period, factor, name) for name in names[:-1]]
     return {"loc": loc, "type": "probability"}
 
 
-def identity_constraints_log_ces(factors, period, all_factors):
+def identity_constraints_log_ces(factors, aug_period, all_factors):
+    """Identity constraints for log_ces."""
     raise NotImplementedError
 
 
@@ -173,8 +175,9 @@ def params_robust_translog(factors):
     return params_translog(factors)
 
 
-def identity_constraints_robust_translog(factor, period, all_factors) -> list[dict]:
-    return identity_constraints_translog(factor, period, all_factors)
+def identity_constraints_robust_translog(factor, aug_period, all_factors) -> list[dict]:
+    """Identity constraints for robust_translog."""
+    return identity_constraints_translog(factor, aug_period, all_factors)
 
 
 def linear_and_squares(states, params):
@@ -196,14 +199,16 @@ def params_linear_and_squares(factors):
     return names
 
 
-def identity_constraints_linear_and_squares(factor, period, all_factors) -> list[dict]:
-    """Index tuples for linear_and_squares transition function."""
+def identity_constraints_linear_and_squares(
+    factor, aug_period, all_factors
+) -> list[dict]:
+    """Identity constraints for linear_and_squares transition function."""
     constraints_dicts = []
     for regressor in params_linear_and_squares(all_factors):
         val = 1.0 if factor == regressor else 0.0
         constraints_dicts.append(
             {
-                "loc": ("transition", period, factor, regressor),
+                "loc": ("transition", aug_period, factor, regressor),
                 "type": "fixed",
                 "value": val,
                 "description": "Identity constraint.",
@@ -234,5 +239,6 @@ def params_log_ces_general(factors):
     return factors + [f"sigma_{fac}" for fac in factors] + ["tfp"]
 
 
-def identity_constraints_log_ces_general(factors, period, all_factors):
+def identity_constraints_log_ces_general(factors, aug_period, all_factors):
+    """Identity constraints for log_ces_general."""
     raise NotImplementedError

From 33bed6a463248e02270485ad7aacad1c17ea276a Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Tue, 7 Oct 2025 05:33:38 +0200
Subject: [PATCH 41/48] Use 'aug_period' also in DataFrame.

---
 src/skillmodels/process_data.py               | 26 +++++++++----------
 .../visualize_transition_equations.py         |  6 ++---
 tests/simplest_augmented_data_expected.csv    |  2 +-
 tests/test_process_data.py                    | 21 ++++++++-------
 4 files changed, 27 insertions(+), 28 deletions(-)

diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index a127eb8..8bd8806 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -76,13 +76,13 @@ def pre_process_data(df, periods):
     df["__old_period__"] = df.index.get_level_values(1)
 
     # replace existing codes for periods and
-    df.index.names = ["id", "period"]
+    df.index.names = ["id", "aug_period"]
     for level in [0, 1]:
         df.index = df.index.set_levels(range(len(df.index.levels[level])), level=level)
 
     # create new index
     ids = sorted(df.index.get_level_values("id").unique())
-    new_index = pd.MultiIndex.from_product([ids, periods], names=["id", "period"])
+    new_index = pd.MultiIndex.from_product([ids, periods], names=["id", "aug_period"])
 
     # set new index
     df = df.reindex(new_index)
@@ -101,13 +101,13 @@ def _get_period_data_for_endogenous_factors(
     controls = labels["controls"]
     observed = labels["observed_factors"]
 
-    out = df.query(f"period == {period}")[
+    out = df.query(f"aug_period == {period}")[
         [
             "id",
             *meas,
             *controls,
             *observed,
-            "period",
+            "aug_period",
             "__old_id__",
             "__old_period__",
         ]
@@ -131,9 +131,9 @@ def _augment_data_for_endogenous_factors(
     df = df.reset_index()
     # Make sure datset is balanced
     n_ids = df["id"].nunique()
-    n_periods = df["period"].nunique()
+    n_periods = df["aug_period"].nunique()
     assert n_ids * n_periods == df.shape[0]
-    assert set(df["period"]) == set(labels["aug_periods_to_periods"].values())
+    assert set(df["aug_period"]) == set(labels["aug_periods_to_periods"].values())
 
     out = pd.concat(
         [
@@ -147,9 +147,7 @@ def _augment_data_for_endogenous_factors(
             for aug_period, period in labels["aug_periods_to_periods"].items()
         ]
     )
-    return (
-        out.set_index(["id", "aug_period"]).rename_axis(["id", "period"]).sort_index()
-    )
+    return out.set_index(["id", "aug_period"]).sort_index()
 
 
 def _add_copies_of_anchoring_outcome(df, anchoring_info):
@@ -163,7 +161,7 @@ def _add_copies_of_anchoring_outcome(df, anchoring_info):
 def _check_data(df, update_info, labels, purpose):  # noqa: C901
     var_report = pd.DataFrame(index=update_info.index[:0], columns=["problem"])
     for aug_period in labels["aug_periods"]:
-        period_data = df.query(f"period == {aug_period}")
+        period_data = df.query(f"aug_period == {aug_period}")
         for cont in labels["controls"]:
             if cont not in period_data.columns or period_data[cont].isna().all():
                 var_report.loc[(aug_period, cont), "problem"] = "Variable is missing"
@@ -197,7 +195,7 @@ def _handle_controls_with_missings(df, controls, update_info):
     aug_periods = update_info.index.get_level_values(0).unique().tolist()
     problematic_index = df.index[:0]
     for aug_period in aug_periods:
-        period_data = df.query(f"period == {aug_period}")
+        period_data = df.query(f"aug_period == {aug_period}")
         control_data = period_data[controls]
         meas_data = period_data[_get_period_measurements(update_info, aug_period)]
         problem = control_data.isna().any(axis=1) & meas_data.notna().any(axis=1)
@@ -223,14 +221,14 @@ def _get_period_measurements(update_info, aug_period):
 def _generate_measurements_array(df, update_info, n_obs):
     arr = np.zeros((len(update_info), n_obs))
     for k, (aug_period, var) in enumerate(update_info.index):
-        arr[k] = df.query(f"period == {aug_period}")[var].to_numpy()
+        arr[k] = df.query(f"aug_period == {aug_period}")[var].to_numpy()
     return jnp.array(arr, dtype="float32")
 
 
 def _generate_controls_array(df, labels, n_obs):
     arr = np.zeros((len(labels["aug_periods"]), n_obs, len(labels["controls"])))
     for aug_period in labels["aug_periods"]:
-        arr[aug_period] = df.query(f"period == {aug_period}")[
+        arr[aug_period] = df.query(f"aug_period == {aug_period}")[
             labels["controls"]
         ].to_numpy()
     return jnp.array(arr, dtype="float32")
@@ -239,7 +237,7 @@ def _generate_controls_array(df, labels, n_obs):
 def _generate_observed_factor_array(df, labels, n_obs):
     arr = np.zeros((len(labels["aug_periods"]), n_obs, len(labels["observed_factors"])))
     for aug_period in labels["aug_periods"]:
-        arr[aug_period] = df.query(f"period == {aug_period}")[
+        arr[aug_period] = df.query(f"aug_period == {aug_period}")[
             labels["observed_factors"]
         ].to_numpy()
     return jnp.array(arr, dtype="float32")
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index e630b28..375eb5e 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -404,7 +404,7 @@ def _get_states_data(model, period, data, states, observed_factors):
                     columns=observed_factors,
                 )
                 df["id"] = df.index
-                df["period"] = aug_p
+                df["aug_period"] = aug_p
                 to_concat.append(df)
             observed_data = pd.concat(to_concat)
         else:
@@ -413,13 +413,13 @@ def _get_states_data(model, period, data, states, observed_factors):
                 columns=observed_factors,
             )
             observed_data["id"] = observed_data.index
-            observed_data["period"] = period
+            observed_data["aug_period"] = period
         # Do a left merge because we need all periods for the ranges
         states_data = pd.merge(
             left=states,
             right=observed_data,
             left_on=["id", "period"],
-            right_on=["id", "period"],
+            right_on=["id", "aug_period"],
             how="left",
         )
     else:
diff --git a/tests/simplest_augmented_data_expected.csv b/tests/simplest_augmented_data_expected.csv
index 5f0b1a6..aab98d2 100644
--- a/tests/simplest_augmented_data_expected.csv
+++ b/tests/simplest_augmented_data_expected.csv
@@ -1,4 +1,4 @@
-id,period,constant,var,inv,of
+id,aug_period,constant,var,inv,of
 0,0,1,0.0,,10.0
 0,1,1,,5.0,10.0
 0,2,1,,,
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index 36a40e8..9450f62 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -55,7 +55,8 @@ def simplest_augmented():
     _df["id"] = [1, 3, 3, 5, 5]
     out["data_input"] = _df.set_index(["id", "period"])
     out["data_exp"] = pd.read_csv(
-        TEST_DIR / "simplest_augmented_data_expected.csv", index_col=["id", "period"]
+        TEST_DIR / "simplest_augmented_data_expected.csv",
+        index_col=["id", "aug_period"],
     )
     return out
 
@@ -81,11 +82,11 @@ def test_handle_controls_with_missings():
     update_info = pd.DataFrame(index=pd.MultiIndex.from_tuples(uinfo_ind_tups))
     data = [[1, 1, 1], [np.nan, 1, 1], [np.nan, 1, np.nan], [np.nan, np.nan, np.nan]]
     df = pd.DataFrame(data=data, columns=["m1", "m2", "c1"])
-    df["period"] = 0
+    df["aug_period"] = 0
     df["id"] = np.arange(4)
     df["__old_id__"] = df["id"]
-    df["__old_period__"] = df["period"] + 1
-    df.set_index(["id", "period"], inplace=True)
+    df["__old_period__"] = df["aug_period"] + 1
+    df.set_index(["id", "aug_period"], inplace=True)
 
     with pytest.warns(UserWarning):
         calculated = _handle_controls_with_missings(df, controls, update_info)
@@ -97,13 +98,13 @@ def test_generate_measurements_array():
     update_info = pd.DataFrame(index=pd.MultiIndex.from_tuples(uinfo_ind_tups))
 
     csv = """
-    id,period,m1,m2,m3
+    id,aug_period,m1,m2,m3
     0,0,1,2,3
     0,1,4,5,6
     1,0,7,8,9
     1,1,10,11,12
     """
-    data = _read_csv_string(csv, ["id", "period"])
+    data = _read_csv_string(csv, ["id", "aug_period"])
 
     expected = jnp.array([[1, 7], [2, 8], [4, 10], [6, 12.0]])
 
@@ -113,13 +114,13 @@ def test_generate_measurements_array():
 
 def test_generate_controls_array():
     csv = """
-    id,period,c1,c2
+    id,aug_period,c1,c2
     0, 0, 1, 2
     0, 1, 3, 4
     1, 0, 5, 8
     1, 1, 7, 8
     """
-    data = _read_csv_string(csv, ["id", "period"])
+    data = _read_csv_string(csv, ["id", "aug_period"])
 
     labels = {"controls": ["c1", "c2"], "aug_periods": [0, 1]}
 
@@ -130,13 +131,13 @@ def test_generate_controls_array():
 
 def test_generate_observed_factor_array():
     csv = """
-    id,period,v1,v2
+    id,aug_period,v1,v2
     0, 0, 1, 2
     0, 1, 3, 4
     1, 0, 5, 8
     1, 1, 7, 8
     """
-    data = _read_csv_string(csv, ["id", "period"])
+    data = _read_csv_string(csv, ["id", "aug_period"])
 
     labels = {"observed_factors": ["v1", "v2"], "aug_periods": [0, 1]}
 

From 0c11662f8c927eb75816434ab0a15f7ec75c29c0 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Tue, 7 Oct 2025 06:05:38 +0200
Subject: [PATCH 42/48] Use 'aug_period' as index also in update_info.

---
 src/skillmodels/correlation_heatmap.py          | 8 ++++----
 src/skillmodels/maximization_inputs.py          | 4 +++-
 src/skillmodels/process_debug_data.py           | 2 +-
 src/skillmodels/process_model.py                | 4 +++-
 tests/model2_correct_update_info.csv            | 2 +-
 tests/model2_with_endog_correct_update_info.csv | 2 +-
 tests/test_process_model.py                     | 4 ++--
 7 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index 84566de..16512dc 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -278,17 +278,17 @@ def _get_update_info_for_periods(model):
     update_info = model["update_info"].copy()
 
     # Replace period level with user-provided period using set_codes
-    period_values = update_info.index.get_level_values("period").map(
+    period_values = update_info.index.get_level_values("aug_period").map(
         model["labels"]["aug_periods_to_periods"]
     )
-    update_info.index = update_info.index.set_codes(period_values, level="period")
+    update_info.index = update_info.index.set_codes(period_values, level="aug_period")
 
     # Group by period and variable, apply OR logic for boolean columns
     cols = [col for col in update_info.columns if col != "purpose"]
     agg_dict = dict.fromkeys(cols, "any")
     agg_dict["purpose"] = "first"
 
-    return update_info.groupby(["period", "variable"]).agg(agg_dict)
+    return update_info.groupby(["aug_period", "variable"]).agg(agg_dict)
 
 
 def _get_measurement_data(data, update_info, periods, latent_factors, observed_factors):
@@ -684,7 +684,7 @@ def _get_factor_scores_data_for_single_model_period(
     if period not in update_info.index:
         return pd.DataFrame()
     period_info = update_info.loc[period].reset_index()
-    params = params.query(f"period=={period}").droplevel("period")
+    params = params.query(f"aug_period=={period}").droplevel("aug_period")
     loadings = params.loc["loadings"]["value"]
     intercepts = (
         params.loc["controls"].query("name2 == 'constant'").droplevel("name2")["value"]
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 5629eba..a392e17 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -204,7 +204,9 @@ def _partial_some_log_likelihood(
 ):
     update_info = model["update_info"]
     is_measurement_iteration = (update_info["purpose"] == "measurement").to_numpy()
-    _aug_periods = pd.Series(update_info.index.get_level_values("period").to_numpy())
+    _aug_periods = pd.Series(
+        update_info.index.get_level_values("aug_period").to_numpy()
+    )
     is_predict_iteration = ((_aug_periods - _aug_periods.shift(-1)) == -1).to_numpy()
     # iteration_to_period is used as an indexer to loop over arrays of different lengths
     # in a jax.lax.scan. It needs to work for arrays of length n_aug_periods and not raise
diff --git a/src/skillmodels/process_debug_data.py b/src/skillmodels/process_debug_data.py
index 92eaa5b..d662dfa 100644
--- a/src/skillmodels/process_debug_data.py
+++ b/src/skillmodels/process_debug_data.py
@@ -132,7 +132,7 @@ def _create_filtered_states(filtered_states, log_mixture_weights, update_info, f
     keep = []
     for i, (aug_period, measurement) in enumerate(update_info.index):
         last_measurement = update_info.query(
-            f"purpose == 'measurement' & period == {aug_period}",
+            f"purpose == 'measurement' & aug_period == {aug_period}",
         ).index[-1][1]
 
         if measurement == last_measurement:
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 6183f0c..08a8e0d 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -458,7 +458,9 @@ def _get_update_info(model_dict, dimensions, labels, anchoring_info):
             the likelihood function. See :ref:`update_info`.
 
     """
-    index = pd.MultiIndex(levels=[[], []], codes=[[], []], names=["period", "variable"])
+    index = pd.MultiIndex(
+        levels=[[], []], codes=[[], []], names=["aug_period", "variable"]
+    )
     uinfo = DataFrame(index=index, columns=labels["latent_factors"] + ["purpose"])
 
     measurements = {}
diff --git a/tests/model2_correct_update_info.csv b/tests/model2_correct_update_info.csv
index de382ca..4f1771c 100644
--- a/tests/model2_correct_update_info.csv
+++ b/tests/model2_correct_update_info.csv
@@ -1,4 +1,4 @@
-period,variable,fac1,fac2,fac3,purpose
+aug_period,variable,fac1,fac2,fac3,purpose
 0,y1,True,False,False,measurement
 0,y2,True,False,False,measurement
 0,y3,True,False,False,measurement
diff --git a/tests/model2_with_endog_correct_update_info.csv b/tests/model2_with_endog_correct_update_info.csv
index 3ca7335..05ae797 100644
--- a/tests/model2_with_endog_correct_update_info.csv
+++ b/tests/model2_with_endog_correct_update_info.csv
@@ -1,4 +1,4 @@
-period,variable,fac1,fac2,fac3,purpose
+aug_period,variable,fac1,fac2,fac3,purpose
 0,y1,True,False,False,measurement
 0,y2,True,False,False,measurement
 0,y3,True,False,False,measurement
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index ace15dc..c7dc796 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -81,7 +81,7 @@ def test_update_info(model2):
     test_dir = Path(__file__).parent.resolve()
     expected = pd.read_csv(
         test_dir / "model2_correct_update_info.csv",
-        index_col=["period", "variable"],
+        index_col=["aug_period", "variable"],
     )
     assert_frame_equal(res, expected)
 
@@ -238,7 +238,7 @@ def test_with_endog_update_info(model2_inv):
     test_dir = Path(__file__).parent.resolve()
     expected = pd.read_csv(
         test_dir / "model2_with_endog_correct_update_info.csv",
-        index_col=["period", "variable"],
+        index_col=["aug_period", "variable"],
     )
     assert_frame_equal(res, expected)
 

From 2c3ac8d453ca1fdc90f5fbc096f0afd82d9dbc2d Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Sun, 12 Oct 2025 07:01:12 +0200
Subject: [PATCH 43/48] More fixes to aug_period / period.

---
 src/skillmodels/constraints.py                |  49 +++++---
 src/skillmodels/correlation_heatmap.py        | 112 +++++++++---------
 src/skillmodels/filtered_states.py            |  20 +++-
 src/skillmodels/process_data.py               |  13 +-
 src/skillmodels/process_debug_data.py         |  14 ++-
 src/skillmodels/process_model.py              |  17 ++-
 src/skillmodels/simulate_data.py              |  95 ++++++++++++---
 .../visualize_factor_distributions.py         |  49 ++++++--
 .../visualize_transition_equations.py         |  45 +++----
 9 files changed, 269 insertions(+), 145 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 9829024..ab57e1d 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -406,17 +406,19 @@ def _get_constraints_for_augmented_periods(
         tname = labels["transition_names"][f]
         if tname == "constant":
             continue
-        aug_period_type_to_constrain = (
-            "endogenous_factors"
+        # We are restricting transitions and shocks, not measurements. So this might
+        # look counterintuitive...
+        aug_period_meas_type_to_constrain = (
+            "states"
             if endogenous_factors_info[factor]["is_state"]
-            else "states"
+            else "endogenous_factors"
         )
         aug_periods_to_constrain = [
             k
             for k, v in endogenous_factors_info[
-                "aug_periods_to_aug_period_types"
+                "aug_periods_to_aug_period_meas_types"
             ].items()
-            if v == aug_period_type_to_constrain
+            if v == aug_period_meas_type_to_constrain
         ]
         for aug_period in aug_periods_to_constrain:
             if func := getattr(t_f_module, f"identity_constraints_{tname}", False):
@@ -444,6 +446,11 @@ def _sel(params, loc):
 
 @dataclass(frozen=True)
 class SkillmodelsPairwiseEqualityConstraint(om.PairwiseEqualityConstraint):
+    """Thin wrapper around om.PairwiseEqualityConstraint.
+
+    Adds fields to preserve information from the internal constraints dictionary.
+    """
+
     loc: pd.MultiIndex | tuple | str | None = None
     description: str | None = None
     type: str = "Just to be able to use **constraints_dict"
@@ -452,6 +459,11 @@ class SkillmodelsPairwiseEqualityConstraint(om.PairwiseEqualityConstraint):
 
 @dataclass(frozen=True)
 class SkillmodelsFixedConstraint(om.FixedConstraint):
+    """Thin wrapper around om.FixedConstraint.
+
+    Adds fields to preserve information from the internal constraints dictionary.
+    """
+
     loc: pd.MultiIndex | tuple | str | None = None
     description: str | None = None
     type: str = "Just to be able to use **constraints_dict"
@@ -461,6 +473,11 @@ class SkillmodelsFixedConstraint(om.FixedConstraint):
 
 @dataclass(frozen=True)
 class SkillmodelsEqualityConstraint(om.EqualityConstraint):
+    """Thin wrapper around om.EqualityConstraint.
+
+    Adds fields to preserve information from the internal constraints dictionary.
+    """
+
     loc: pd.MultiIndex | tuple | str | None = None
     description: str | None = None
     type: str = "Just to be able to use **constraints_dict"
@@ -469,6 +486,11 @@ class SkillmodelsEqualityConstraint(om.EqualityConstraint):
 
 @dataclass(frozen=True)
 class SkillmodelsProbabilityConstraint(om.ProbabilityConstraint):
+    """Thin wrapper around om.ProbabilityConstraint.
+
+    Adds fields to preserve information from the internal constraints dictionary.
+    """
+
     loc: pd.MultiIndex | tuple | str | None = None
     description: str | None = None
     type: str = "Just to be able to use **constraints_dict"
@@ -477,6 +499,11 @@ class SkillmodelsProbabilityConstraint(om.ProbabilityConstraint):
 
 @dataclass(frozen=True)
 class SkillmodelsIncreasingConstraint(om.IncreasingConstraint):
+    """Thin wrapper around om.IncreasingConstraint.
+
+    Adds fields to preserve information from the internal constraints dictionary.
+    """
+
     loc: pd.MultiIndex | tuple | str | None = None
     description: str | None = None
     type: str = "Just to be able to use **constraints_dict"
@@ -488,15 +515,12 @@ def constraints_dicts_to_om(
 ) -> list[om.constraints.Constraint]:
     """Convert constraints provided in dictionary form to optimagic constraints.
 
-    Each dictionary requires an
-
     Args:
         constraints_dicts (list): see :ref:`get_constraints_dicts`.
 
     Returns:
         List of optimagic constraints.
     """
-    return constraints_dicts
     om_style = []
     for c_d in constraints_dicts:
         if c_d["type"] == "pairwise_equality":
@@ -546,15 +570,6 @@ def enforce_fixed_constraints(
         for constraint in constraints_dicts:
             if constraint["type"] == "fixed":
                 params.loc[constraint["loc"], "value"] = constraint["value"]
-                params.loc[constraint["loc"], "lower_bound"] = np.nextafter(
-                    constraint["value"], -np.inf
-                )
-                params.loc[constraint["loc"], "upper_bound"] = constraint["value"]
 
-    # Check that fixed constraints are valid
-    fixed = params[params["value"].notna()]
-    invalid = fixed.query("value < lower_bound or value > upper_bound")
-    if len(invalid) > 0:
-        raise ValueError(f"Invalid fixed constraints:\n\n{invalid}")
     # Setting via loc may expand the index, so reduce to the original index
     return params.loc[params_template.index].astype(float)
diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index 16512dc..67e4593 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -148,10 +148,10 @@ def get_measurements_corr(data, model_dict, factors, periods):
     periods = _process_periods(periods, model)
     processed_data = pre_process_data(data, periods)
     latent_factors, observed_factors = _process_factors(model, factors)
-    update_info = _get_update_info_for_periods(model)
+    update_info_by_period = _get_update_info_for_periods(model)
     df = _get_measurement_data(
         data=processed_data,
-        update_info=update_info,
+        update_info_by_period=update_info_by_period,
         periods=periods,
         latent_factors=latent_factors,
         observed_factors=observed_factors,
@@ -192,7 +192,7 @@ def get_quasi_scores_corr(data, model_dict, factors, periods):
     update_info = _get_update_info_for_periods(model)
     df = _get_quasi_factor_scores_data(
         data=processed_data,
-        update_info=update_info,
+        update_info_by_period=update_info,
         periods=periods,
         latent_factors=latent_factors,
         observed_factors=observed_factors,
@@ -274,7 +274,7 @@ def _get_mask(corr, show_upper_triangle, show_diagonal):
 
 
 def _get_update_info_for_periods(model):
-    """Transform update_info to use user-provided periods instead of augmented periods."""
+    """Return update_info with user-provided periods instead of augmented periods."""
     update_info = model["update_info"].copy()
 
     # Replace period level with user-provided period using set_codes
@@ -282,16 +282,19 @@ def _get_update_info_for_periods(model):
         model["labels"]["aug_periods_to_periods"]
     )
     update_info.index = update_info.index.set_codes(period_values, level="aug_period")
+    update_info.index = update_info.index.set_names(["period", "variable"])
 
     # Group by period and variable, apply OR logic for boolean columns
     cols = [col for col in update_info.columns if col != "purpose"]
     agg_dict = dict.fromkeys(cols, "any")
     agg_dict["purpose"] = "first"
 
-    return update_info.groupby(["aug_period", "variable"]).agg(agg_dict)
+    return update_info.groupby(["period", "variable"]).agg(agg_dict)
 
 
-def _get_measurement_data(data, update_info, periods, latent_factors, observed_factors):
+def _get_measurement_data(
+    data, update_info_by_period, periods, latent_factors, observed_factors
+):
     """Get data frame with factor measurements in each period, in wide format.
 
     For each factor, retrieve the data on measurements in each period and stack
@@ -315,26 +318,26 @@ def _get_measurement_data(data, update_info, periods, latent_factors, observed_f
     if len(periods) == 1:
         period = periods[0]
         df = _get_measurement_data_for_single_period(
-            data,
-            update_info,
-            period,
-            latent_factors,
-            observed_factors,
+            data=data,
+            update_info_by_period=update_info_by_period,
+            period=period,
+            latent_factors=latent_factors,
+            observed_factors=observed_factors,
         )
     else:
         df = _get_measurement_data_for_multiple_periods(
-            data,
-            update_info,
-            periods,
-            latent_factors,
-            observed_factors,
+            data=data,
+            update_info_by_period=update_info_by_period,
+            periods=periods,
+            latent_factors=latent_factors,
+            observed_factors=observed_factors,
         )
     return df
 
 
 def _get_measurement_data_for_single_period(
     data,
-    update_info,
+    update_info_by_period,
     period,
     latent_factors,
     observed_factors,
@@ -355,7 +358,7 @@ def _get_measurement_data_for_single_period(
         df (pd.DataFrame): DataFrame with measurements of factors for period 'period'.
 
     """
-    period_info = update_info.loc[period].reset_index()
+    period_info = update_info_by_period.loc[period].reset_index()
     measurements = []
 
     for fac in latent_factors:
@@ -364,13 +367,13 @@ def _get_measurement_data_for_single_period(
         )["variable"].to_list()
     for fac in observed_factors:
         measurements.append(fac)
-    df = data.query(f"{update_info.index.names[0]}=={period}")[measurements]
+    df = data.query(f"{update_info_by_period.index.names[0]}=={period}")[measurements]
     return df
 
 
 def _get_measurement_data_for_multiple_periods(
     data,
-    update_info,
+    update_info_by_period,
     periods,
     latent_factors,
     observed_factors,
@@ -379,8 +382,8 @@ def _get_measurement_data_for_multiple_periods(
 
     Args:
         data (pd.DataFrame): Data with observable variables.
-        update_info (pd.DataFrame): DataFrame with information on measurements
-            for each factor in each model period.
+        update_info_by_period (pd.DataFrame): DataFrame with information on measurements
+            for each factor in each user-provided period.
         periods (list): The periods to extract measurements for.
         latent_factors (list): List of latent factors the measurements of which
             correlations are calculated for.
@@ -396,11 +399,11 @@ def _get_measurement_data_for_multiple_periods(
     for period in periods:
         to_concat.append(
             _get_measurement_data_for_single_period(
-                data,
-                update_info,
-                period,
-                latent_factors,
-                observed_factors,
+                data=data,
+                update_info_by_period=update_info_by_period,
+                period=period,
+                latent_factors=latent_factors,
+                observed_factors=observed_factors,
             )
             .add_suffix(f", {period}")
             .reset_index(drop=True),
@@ -411,7 +414,7 @@ def _get_measurement_data_for_multiple_periods(
 
 def _get_quasi_factor_scores_data(
     data,
-    update_info,
+    update_info_by_period,
     periods,
     latent_factors,
     observed_factors,
@@ -442,7 +445,7 @@ def _get_quasi_factor_scores_data(
         period = periods[0]
         df = _get_quasi_factor_scores_data_for_single_period(
             data,
-            update_info,
+            update_info_by_period,
             period,
             latent_factors,
             observed_factors,
@@ -450,7 +453,7 @@ def _get_quasi_factor_scores_data(
     else:
         df = _get_quasi_factor_scores_data_for_multiple_periods(
             data,
-            update_info,
+            update_info_by_period,
             periods,
             latent_factors,
             observed_factors,
@@ -461,7 +464,7 @@ def _get_quasi_factor_scores_data(
 
 def _get_quasi_factor_scores_data_for_single_period(
     data,
-    update_info,
+    update_info_by_period,
     period,
     latent_factors,
     observed_factors,
@@ -470,8 +473,8 @@ def _get_quasi_factor_scores_data_for_single_period(
 
     Args:
         data (pd.DataFrame): Data with observable variables.
-        update_info (pd.DataFrame): DataFrame with information on measurements
-            for each factor in each model period.
+        update_info_by_period (pd.DataFrame): DataFrame with information on measurements
+            for each factor in each user-provided period.
         periods (list): The list of periods that correlations are
             calculated for.
         latent_factors (list): List of latent factors the scores of which
@@ -483,13 +486,13 @@ def _get_quasi_factor_scores_data_for_single_period(
         df (pd.DataFrame): Processed DataFrame to calculate correlations over.
 
     """
-    period_info = update_info.loc[period].reset_index()
+    period_info = update_info_by_period.loc[period].reset_index()
     to_concat = []
     for factor in latent_factors:
         period_factor_measurements = period_info.query(
             f"{factor} == True and purpose == 'measurement'",
         )["variable"].to_list()
-        df = data.query(f"{update_info.index.names[0]}=={period}")[
+        df = data.query(f"{update_info_by_period.index.names[0]}=={period}")[
             period_factor_measurements
         ]
         df = (df - df.mean()) / df.std()
@@ -497,7 +500,7 @@ def _get_quasi_factor_scores_data_for_single_period(
         sr.name = f"{factor}"
         to_concat.append(sr)
     for factor in observed_factors:
-        df = data.query(f"{update_info.index.names[0]}=={period}")[factor]
+        df = data.query(f"{update_info_by_period.index.names[0]}=={period}")[factor]
         to_concat.append(df)
     df = pd.concat(to_concat, axis=1)
     return df
@@ -505,7 +508,7 @@ def _get_quasi_factor_scores_data_for_single_period(
 
 def _get_quasi_factor_scores_data_for_multiple_periods(
     data,
-    update_info,
+    update_info_by_period,
     periods,
     latent_factors,
     observed_factors,
@@ -514,8 +517,8 @@ def _get_quasi_factor_scores_data_for_multiple_periods(
 
     Args:
         data (pd.DataFrame): Data with observable variables.
-        update_info (pd.DataFrame): DataFrame with information on measurements
-            for each factor in each model period.
+        update_info_by_period (pd.DataFrame): DataFrame with information on measurements
+            for each factor in each user-provided period.
         periods (list): The list of periods that correlations are
             calculated for.
         latent_factors (list): List of latent factors the scores of which
@@ -532,7 +535,7 @@ def _get_quasi_factor_scores_data_for_multiple_periods(
         to_concat.append(
             _get_quasi_factor_scores_data_for_single_period(
                 data,
-                update_info,
+                update_info_by_period,
                 period,
                 latent_factors,
                 observed_factors,
@@ -626,23 +629,19 @@ def _get_factor_scores_data_for_single_period(
         df (pd.DataFrame): Processed DataFrame to calculate correlations over.
 
     """
-    model_periods = [
-        aug_p
-        for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
-        if p == period
-    ]
+    aug_periods = model["endogenous_factors_info"]["aug_periods_from_period"](period)
     df = pd.concat(
         [
             _get_factor_scores_data_for_single_model_period(
                 data=data,
                 params=params,
                 update_info=model["update_info"],
-                period=mp,
-                period_raw=period,
+                aug_period=ap,
+                period=period,
                 latent_factors=latent_factors,
                 observed_factors=observed_factors,
             )
-            for mp in model_periods
+            for ap in aug_periods
         ],
         axis=0,
     )
@@ -658,8 +657,8 @@ def _get_factor_scores_data_for_single_model_period(
     data,
     params,
     update_info,
+    aug_period,
     period,
-    period_raw,
     latent_factors,
     observed_factors,
 ):
@@ -672,7 +671,8 @@ def _get_factor_scores_data_for_single_model_period(
         params (pd.DataFrame): Data frame with estimated measurement relevant
         update_info (pd.DataFrame): DataFrame with information on measurements
             for each factor in each model period.
-        period (int): The (augmented) period that correlations are calculated for.
+        aug_period (int): The (augmented) period that correlations are calculated for.
+        period (int): The (raw) period that correlations are calculated for.
         latent_factors (list): List of latent factors the scores of which
             correlations are calculated for.
         observed_factors (list): List of observed factors the scores of which
@@ -681,10 +681,10 @@ def _get_factor_scores_data_for_single_model_period(
     Returns:
         df (pd.DataFrame): Processed DataFrame to calculate correlations over.
     """
-    if period not in update_info.index:
+    if aug_period not in update_info.index:
         return pd.DataFrame()
-    period_info = update_info.loc[period].reset_index()
-    params = params.query(f"aug_period=={period}").droplevel("aug_period")
+    period_info = update_info.loc[aug_period].reset_index()
+    params = params.query(f"aug_period=={aug_period}").droplevel("aug_period")
     loadings = params.loc["loadings"]["value"]
     intercepts = (
         params.loc["controls"].query("name2 == 'constant'").droplevel("name2")["value"]
@@ -699,16 +699,14 @@ def _get_factor_scores_data_for_single_model_period(
         period_factor_measurements = [
             m for m in period_factor_measurements if m not in leave_out_meas
         ]
-        df = data.query(f"{update_info.index.names[0]}=={period_raw}")[
-            period_factor_measurements
-        ]
+        df = data.query(f"period == {period}")[period_factor_measurements]
         for m in period_factor_measurements:
             df[m] = (df[m] - intercepts.loc[m]) / loadings.loc[(m, factor)]
         sr = df.mean(axis=1)
         sr.name = f"{factor}"
         to_concat.append(sr)
     for factor in observed_factors:
-        df = data.query(f"{update_info.index.names[0]}=={period_raw}")[factor]
+        df = data.query(f"period == {period}")[factor]
         to_concat.append(df)
     return pd.concat(to_concat, axis=1)
 
diff --git a/src/skillmodels/filtered_states.py b/src/skillmodels/filtered_states.py
index cbf51f9..2efb4c6 100644
--- a/src/skillmodels/filtered_states.py
+++ b/src/skillmodels/filtered_states.py
@@ -21,6 +21,7 @@ def get_filtered_states(model_dict, data, params):
         states_df=unanchored_states_df,
         model_dict=model_dict,
         params=params,
+        use_aug_period=True,
     )
 
     anchored_ranges = create_state_ranges(
@@ -42,7 +43,7 @@ def get_filtered_states(model_dict, data, params):
     return out
 
 
-def anchor_states_df(states_df, model_dict, params):
+def anchor_states_df(states_df, model_dict, params, use_aug_period):
     """Anchor states in a DataFrame.
 
     The DataFrame is expected to have a column called "period" as well as one column
@@ -86,10 +87,21 @@ def anchor_states_df(states_df, model_dict, params):
 
     n_latent = model["dimensions"]["n_latent_factors"]
 
-    scaling_factors = np.array(pardict["anchoring_scaling_factors"][:, :n_latent])
-    constants = np.array(pardict["anchoring_constants"][:, :n_latent])
+    _scaling_factors = np.array(pardict["anchoring_scaling_factors"][:, :n_latent])
+    _constants = np.array(pardict["anchoring_constants"][:, :n_latent])
+    if use_aug_period:
+        period_arr = states_df["aug_period"].to_numpy()
+        ap_to_p = model["labels"]["aug_periods_to_periods"]
+        scaling_factors = np.empty(shape=(len(ap_to_p), n_latent))
+        constants = np.empty(shape=(len(ap_to_p), n_latent))
+        for ap, p in ap_to_p.items():
+            scaling_factors[ap] = _scaling_factors[p]
+            constants[ap] = _constants[p]
+    else:
+        period_arr = states_df["period"].to_numpy()
+        scaling_factors = _scaling_factors
+        constants = _constants
 
-    period_arr = states_df["period"].to_numpy()
     scaling_arr = scaling_factors[period_arr]
     constants_arr = constants[period_arr]
 
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 8bd8806..6450f71 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -47,6 +47,7 @@ def process_data(
         df = _augment_data_for_endogenous_factors(df, labels, update_info)
     else:
         df = _add_copies_of_anchoring_outcome(df, anchoring_info)
+
     _check_data(df, update_info, labels, purpose=purpose)
     n_obs = int(len(df) / len(labels["aug_periods"]))
     df = _handle_controls_with_missings(df, labels["controls"], update_info)
@@ -76,13 +77,13 @@ def pre_process_data(df, periods):
     df["__old_period__"] = df.index.get_level_values(1)
 
     # replace existing codes for periods and
-    df.index.names = ["id", "aug_period"]
+    df.index.names = ["id", "period"]
     for level in [0, 1]:
         df.index = df.index.set_levels(range(len(df.index.levels[level])), level=level)
 
     # create new index
     ids = sorted(df.index.get_level_values("id").unique())
-    new_index = pd.MultiIndex.from_product([ids, periods], names=["id", "aug_period"])
+    new_index = pd.MultiIndex.from_product([ids, periods], names=["id", "period"])
 
     # set new index
     df = df.reindex(new_index)
@@ -101,13 +102,13 @@ def _get_period_data_for_endogenous_factors(
     controls = labels["controls"]
     observed = labels["observed_factors"]
 
-    out = df.query(f"aug_period == {period}")[
+    out = df.query(f"period == {period}")[
         [
             "id",
             *meas,
             *controls,
             *observed,
-            "aug_period",
+            "period",
             "__old_id__",
             "__old_period__",
         ]
@@ -131,9 +132,9 @@ def _augment_data_for_endogenous_factors(
     df = df.reset_index()
     # Make sure datset is balanced
     n_ids = df["id"].nunique()
-    n_periods = df["aug_period"].nunique()
+    n_periods = df["period"].nunique()
     assert n_ids * n_periods == df.shape[0]
-    assert set(df["aug_period"]) == set(labels["aug_periods_to_periods"].values())
+    assert set(df["period"]) == set(labels["aug_periods_to_periods"].values())
 
     out = pd.concat(
         [
diff --git a/src/skillmodels/process_debug_data.py b/src/skillmodels/process_debug_data.py
index d662dfa..1d5e7dd 100644
--- a/src/skillmodels/process_debug_data.py
+++ b/src/skillmodels/process_debug_data.py
@@ -99,7 +99,7 @@ def _create_post_update_states(filtered_states, factors, update_info):
         update_info.index, filtered_states, strict=False
     ):
         df = _convert_state_array_to_df(data, factors)
-        df["period"] = aug_period
+        df["aug_period"] = aug_period
         df["id"] = np.arange(len(df))
         df["measurement"] = meas
         to_concat.append(df)
@@ -141,7 +141,7 @@ def _create_filtered_states(filtered_states, log_mixture_weights, update_info, f
     to_concat = []
     for period, i in enumerate(keep):
         df = pd.DataFrame(data=agg_states[i], columns=factors)
-        df["period"] = period
+        df["aug_period"] = period
         df["id"] = np.arange(len(df))
         to_concat.append(df)
 
@@ -152,8 +152,10 @@ def _create_filtered_states(filtered_states, log_mixture_weights, update_info, f
 
 def create_state_ranges(filtered_states, factors):
     ranges = {}
-    minima = filtered_states.groupby("period").min()
-    maxima = filtered_states.groupby("period").max()
+    # Group by whichever period column is present
+    period_col = "aug_period" if "aug_period" in filtered_states.columns else "period"
+    minima = filtered_states.groupby(period_col).min()
+    maxima = filtered_states.groupby(period_col).max()
     for factor in factors:
         df = pd.concat([minima[factor], maxima[factor]], axis=1)
         df.columns = ["minimum", "maximum"]
@@ -167,7 +169,7 @@ def _process_residuals(residuals, update_info):
     for (aug_period, meas), data in zip(update_info.index, residuals, strict=False):
         df = pd.DataFrame(data.reshape(-1, 1), columns=["residual"])
         df["mixture"] = np.full((n_obs, n_mixtures), np.arange(n_mixtures)).flatten()
-        df["period"] = aug_period
+        df["aug_period"] = aug_period
         df["id"] = np.arange(len(df))
         df["measurement"] = meas
         to_concat.append(df)
@@ -185,7 +187,7 @@ def _process_all_contributions(all_contributions, update_info):
     ):
         df = pd.DataFrame(data=contribs.reshape(-1, 1), columns=["contribution"])
         df["measurement"] = meas
-        df["period"] = period
+        df["aug_period"] = period
         df["id"] = np.arange(len(df))
         to_concat.append(df)
     return pd.concat(to_concat)
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 08a8e0d..7824b78 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -155,6 +155,13 @@ def _get_aug_periods_to_periods(
     )
 
 
+def _aug_periods_from_period(
+    period: int, aug_periods_to_periods: dict[int, int]
+) -> list[int]:
+    """The inverse of the the aug_periods_to_periods mapper."""
+    return [ap for ap, p in aug_periods_to_periods.items() if p == period]
+
+
 def _get_labels(model_dict, has_endogenous_factors, dimensions):
     """Extract labels of the model quantities.
 
@@ -289,7 +296,7 @@ def _insert_empty_elements_into_list(old, insert_at_modulo, to_insert, aug_p_to_
 def _augment_periods_for_endogenous_factors(
     model_dict: dict[str, Any], dimensions: dict[str, Any], labels: dict[str, Any]
 ) -> dict[str, Any]:
-    """Insert periods without measurements / normalisations if endogenous factors are present.
+    """Augment periods if endogenous factors are present.
 
     Args:
         model_dict: The model specification. See: :ref:`model_specs`
@@ -414,11 +421,15 @@ def _get_endogenous_factors_info(
     """Collect information about endogenous factors."""
     endogenous_factors_info = {
         "has_endogenous_factors": has_endogenous_factors,
-        "aug_periods_to_aug_period_types": _get_aug_periods_to_aug_period_types(
+        "aug_periods_to_aug_period_meas_types": _get_aug_periods_to_aug_period_meas_types(  # noqa: E501
             aug_periods=labels["aug_periods_to_periods"].keys(),
             has_endogenous_factors=has_endogenous_factors,
         ),
         "bounds_distance": bounds_distance,
+        "aug_periods_from_period": partial(
+            _aug_periods_from_period,
+            aug_periods_to_periods=labels["aug_periods_to_periods"],
+        ),
     }
     for fac, v in model_dict["factors"].items():
         endogenous_factors_info[fac] = {
@@ -431,7 +442,7 @@ def _get_endogenous_factors_info(
     return endogenous_factors_info
 
 
-def _get_aug_periods_to_aug_period_types(
+def _get_aug_periods_to_aug_period_meas_types(
     aug_periods: list[int], has_endogenous_factors: bool
 ) -> dict[int, Literal["states", "endogenous_factors"]]:
     return {
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index 32b7f87..3ae3879 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -61,7 +61,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
             purpose="simulation",
         )
         control_data = processed_data["controls"]
-        observed_factor_data = processed_data["observed_factors"]
+        observed_factors = processed_data["observed_factors"]
         data_n_obs = control_data.shape[1]
 
         if n_obs is not None and data_n_obs != n_obs:
@@ -74,7 +74,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
     else:
         control_data = jnp.ones((n_obs, 1))
         n_periods = model["dimensions"]["n_periods"]
-        observed_factor_data = jnp.zeros((n_periods, n_obs, 0))
+        observed_factors = jnp.zeros((n_periods, n_obs, 0))
 
     params_index = get_params_index(
         update_info=model["update_info"],
@@ -104,7 +104,7 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         n_obs=n_obs,
     )
 
-    observed_factor_data, latent_data = _simulate_dataset(
+    aug_measurements, aug_latent_data = _simulate_dataset(
         latent_states=states,
         covs=covs,
         log_weights=log_weights,
@@ -117,15 +117,25 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
         ],
         update_info=model["update_info"],
         control_data=control_data,
-        observed_factor_data=observed_factor_data,
+        observed_factors=observed_factors,
         policies=policies,
         transition_info=model["transition_info"],
     )
 
+    # Create collapsed versions with user-facing periods
+    latent_data = _collapse_aug_periods_to_periods(
+        df=aug_latent_data,
+        factors=model["labels"]["latent_factors"],
+        aug_periods_to_periods=model["labels"]["aug_periods_to_periods"],
+        endogenous_factors_info=model["endogenous_factors_info"],
+    )
+
+    # Anchor the collapsed version (anchoring only works with period, not aug_period)
     anchored_latent_data = anchor_states_df(
         states_df=latent_data,
         model_dict=model_dict,
         params=params,
+        use_aug_period=False,
     )
 
     out = {
@@ -143,7 +153,14 @@ def simulate_dataset(model_dict, params, n_obs=None, data=None, policies=None):
                 model["labels"]["latent_factors"],
             ),
         },
-        "measurements": observed_factor_data,
+        "aug_unanchored_states": {
+            "states": aug_latent_data,
+            "state_ranges": create_state_ranges(
+                aug_latent_data,
+                model["labels"]["latent_factors"],
+            ),
+        },
+        "aug_measurements": aug_measurements,
     }
 
     return out
@@ -160,7 +177,7 @@ def _simulate_dataset(
     has_endogenous_factors,
     update_info,
     control_data,
-    observed_factor_data,
+    observed_factors,
     policies,
     transition_info,
 ):
@@ -177,9 +194,9 @@ def _simulate_dataset(
 
     n_states = dimensions["n_latent_factors"]
     if has_endogenous_factors:
-        n_periods = dimensions["n_aug_periods"] - 2
+        n_aug_periods = dimensions["n_aug_periods"] - 1
     else:
-        n_periods = dimensions["n_aug_periods"]
+        n_aug_periods = dimensions["n_aug_periods"]
 
     weights = np.exp(log_weights)[0]
     loadings_df = pd.DataFrame(
@@ -209,12 +226,12 @@ def _simulate_dataset(
         }
         dist_args.append(args)
 
-    latent_states = np.zeros((n_periods, n_obs, n_states))
+    latent_states = np.zeros((n_aug_periods, n_obs, n_states))
     latent_states[0] = generate_start_states(n_obs, dimensions, dist_args, weights)
 
-    for t in range(n_periods - 1):
+    for t in range(n_aug_periods - 1):
         # if there is a shock in period t, add it here
-        policies_t = [p for p in policies if p["period"] == t]
+        policies_t = [p for p in policies if p["aug_period"] == t]
         for policy in policies_t:
             position = labels["latent_factors"].index(policy["factor"])
             latent_states[t, :, position] += _get_shock(
@@ -224,7 +241,7 @@ def _simulate_dataset(
             )
 
         # get combined states and observed factors as jax array
-        to_concat = [latent_states[t], observed_factor_data[t]]
+        to_concat = [latent_states[t], observed_factors[t]]
         states = jnp.array(np.concatenate(to_concat, axis=-1))
         # reshaping is just needed for transform sigma points
         states = states.reshape(1, 1, *states.shape)
@@ -261,7 +278,7 @@ def _simulate_dataset(
 
     observed_data_by_period = []
 
-    for t in range(n_periods):
+    for t in range(n_aug_periods):
         meas = pd.DataFrame(
             data=measurements_from_states(
                 latent_states[t],
@@ -272,26 +289,66 @@ def _simulate_dataset(
             ),
             columns=loadings_df.loc[t].index,
         )
-        meas["period"] = t
+        meas["aug_period"] = t
         observed_data_by_period.append(meas)
 
     observed_data = pd.concat(observed_data_by_period, axis=0, sort=True)
     observed_data["id"] = observed_data.index
-    observed_data = observed_data.sort_values(["id", "period"])
+    observed_data = observed_data.sort_values(["id", "aug_period"])
 
     latent_data_by_period = []
-    for t in range(n_periods):
+    for t in range(n_aug_periods):
         lat = pd.DataFrame(data=latent_states[t], columns=labels["latent_factors"])
-        lat["period"] = t
+        lat["aug_period"] = t
         latent_data_by_period.append(lat)
 
     latent_data = pd.concat(latent_data_by_period, axis=0, sort=True)
     latent_data["id"] = latent_data.index
-    latent_data = latent_data.sort_values(["id", "period"])
-
+    latent_data = latent_data.sort_values(["id", "aug_period"])
     return observed_data, latent_data
 
 
+def _collapse_aug_periods_to_periods(
+    df, factors, aug_periods_to_periods, endogenous_factors_info
+):
+    """Collapse dataframe with aug_period index to user-facing period index.
+
+    For each factor, extracts from the appropriate aug_period based on is_endogenous.
+
+    Args:
+        df (pd.DataFrame): DataFrame with columns "aug_period" and "id"
+        latent_factors (list): List of latent factors
+        aug_periods_to_periods (dict): Mapping from aug_period to period
+        endogenous_factors_info (dict): Information about which factors are endogenous
+
+    Returns:
+        pd.DataFrame: DataFrame with "period" column instead of "aug_period"
+    """
+    df = df.copy()
+    if not endogenous_factors_info["has_endogenous_factors"]:
+        return df.rename(columns={"aug_period": "period"})
+
+    df["period"] = df["aug_period"].map(aug_periods_to_periods)
+    df["_aug_period_meas_type"] = df["aug_period"].map(
+        endogenous_factors_info["aug_periods_to_aug_period_meas_types"]
+    )
+
+    endogenous_cols = [
+        fac for fac in factors if endogenous_factors_info[fac]["is_endogenous"]
+    ]
+    state_cols = [fac for fac in factors if fac not in endogenous_cols]
+
+    out = df.query('_aug_period_meas_type == "endogenous_factors"')[
+        ["id", "period", *endogenous_cols]
+    ]
+    return pd.merge(
+        out,
+        df.query('_aug_period_meas_type == "states"')[["id", "period", *state_cols]],
+        on=["id", "period"],
+        how="outer",
+    )
+
+
 def _get_shock(mean, sd, size):
     """Add stochastic effect to a  factor of length n_obs.
 
diff --git a/src/skillmodels/visualize_factor_distributions.py b/src/skillmodels/visualize_factor_distributions.py
index e5cfc3b..54e6dff 100644
--- a/src/skillmodels/visualize_factor_distributions.py
+++ b/src/skillmodels/visualize_factor_distributions.py
@@ -217,7 +217,13 @@ def univariate_densities(
         observed_factors=observed_factors,
     )
     observed_states = _get_data_observed_factors(data, factors)
-    df = _process_data(states, period, factors, observed_states)
+    df = _process_data(
+        states=states,
+        period=period,
+        factors=factors,
+        aug_periods_to_periods=model["labels"]["aug_periods_to_periods"],
+        observed_states=observed_states,
+    )
     scenarios = df["scenario"].unique()
     plots_dict = {}
     distplot_kwargs = _process_distplot_kwargs(
@@ -322,7 +328,13 @@ def bivariate_density_contours(
         observed_factors=observed_factors,
     )
     observed_states = _get_data_observed_factors(data=data, factors=factors)
-    df = _process_data(states, period, factors, observed_states)
+    df = _process_data(
+        states=states,
+        period=period,
+        factors=factors,
+        aug_periods_to_periods=model["labels"]["aug_periods_to_periods"],
+        observed_states=observed_states,
+    )
     plots_dict = {}
     contour_kwargs = _process_contour_kwargs(
         contour_kwargs,
@@ -441,7 +453,13 @@ def bivariate_density_surfaces(
         observed_factors=observed_factors,
     )
     observed_states = _get_data_observed_factors(data, factors)
-    df = _process_data(states, period, factors, observed_states)
+    df = _process_data(
+        states=states,
+        period=period,
+        factors=factors,
+        aug_periods_to_periods=model["labels"]["aug_periods_to_periods"],
+        observed_states=observed_states,
+    )
     plots_dict = {}
     layout_kwargs = _process_layout_kwargs_3d(
         layout_kwargs,
@@ -486,12 +504,22 @@ def bivariate_density_surfaces(
     return plots_dict
 
 
-def _process_data(states, period, factors, observed_states=None):
+def _process_data(
+    states, period, factors, aug_periods_to_periods, observed_states=None
+):
+    ap_to_p = pd.Series(aug_periods_to_periods, name="period")
+    ap_to_p.index.name = "aug_period"
     if isinstance(states, pd.DataFrame):
+        one_state_per_period = (
+            states.merge(ap_to_p, left_on="aug_period", right_index=True, how="left")
+            .sort_values(["aug_period", "id"])
+            .groupby(["period", "id"])
+            .last()
+        )
         to_concat = []
         for fac in factors:
-            if fac in states:
-                to_concat.append(states.query(f"period == {period}")[fac])
+            if fac in one_state_per_period:
+                to_concat.append(one_state_per_period.query(f"period == {period}")[fac])
         data = pd.concat(to_concat, axis=1)
         data["scenario"] = "none"
     else:
@@ -499,12 +527,19 @@ def _process_data(states, period, factors, observed_states=None):
             states = dict(enumerate(states))
         to_concat = []
         for name, df in states.items():
-            to_keep = df.query(f"period == {period}")[factors].copy()
+            one_state_per_period = (
+                df.merge(ap_to_p, left_on="aug_period", right_index=True, how="left")
+                .sort_values(["aug_period", "id"])
+                .groupby(["period", "id"])
+                .last()
+            )
+            to_keep = one_state_per_period.query(f"period == {period}")[factors].copy()
             to_keep["scenario"] = name
             to_concat.append(to_keep)
         data = pd.concat(to_concat)
     data = data.reset_index()
     if observed_states is not None:
+        # Not sure whether this will continue to work... But no test case right now.
         data = pd.concat(
             [data, observed_states.query(f"period == {period}").reset_index()],
             axis=1,
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 375eb5e..d6eef74 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -268,25 +268,29 @@ def _get_dictionary_with_plots(
         title_kwargs=None,
         showlegend=showlegend,
     )
+    _aug_periods = model["endogenous_factors_info"]["aug_periods_from_period"](period)
     plots_dict = {}
     for output_factor, input_factor in itertools.product(latent_factors, all_factors):
         transition_function = model["transition_info"]["individual_functions"][
             output_factor
         ]
+        if model["endogenous_factors_info"][output_factor]["is_endogenous"]:
+            aug_period = min(_aug_periods)
+        else:
+            aug_period = max(_aug_periods)
         transition_params = {
-            output_factor: pardict["transition"][output_factor][period],
+            output_factor: pardict["transition"][output_factor][aug_period]
         }
 
         if quantiles_of_other_factors is not None:
             plot_data = _prepare_data_for_one_plot_fixed_quantile_2d(
                 states_data=states_data,
                 state_ranges=state_ranges,
-                period=period,
+                aug_period=aug_period,
                 input_factor=input_factor,
                 output_factor=output_factor,
-                quantiles_of_other_factors=quantiles_of_other_factors,
-                model=model,
                 n_points=n_points,
+                quantiles_of_other_factors=quantiles_of_other_factors,
                 transition_function=transition_function,
                 transition_params=transition_params,
                 all_factors=all_factors,
@@ -296,10 +300,9 @@ def _get_dictionary_with_plots(
             plot_data = _prepare_data_for_one_plot_average_2d(
                 states_data=states_data,
                 state_ranges=state_ranges,
-                period=period,
+                aug_period=aug_period,
                 input_factor=input_factor,
                 output_factor=output_factor,
-                model=model,
                 n_points=n_points,
                 n_draws=n_draws,
                 transition_function=transition_function,
@@ -418,7 +421,7 @@ def _get_states_data(model, period, data, states, observed_factors):
         states_data = pd.merge(
             left=states,
             right=observed_data,
-            left_on=["id", "period"],
+            left_on=["id", "aug_period"],
             right_on=["id", "aug_period"],
             how="left",
         )
@@ -430,19 +433,18 @@ def _get_states_data(model, period, data, states, observed_factors):
 def _prepare_data_for_one_plot_fixed_quantile_2d(
     states_data,
     state_ranges,
-    period,
+    aug_period,
     input_factor,
     output_factor,
-    quantiles_of_other_factors,
-    model,
     n_points,
+    quantiles_of_other_factors,
     transition_function,
     transition_params,
     all_factors,
 ):
-    period_data = states_data.query(f"period == {period}")[all_factors]
-    input_min = state_ranges[input_factor].loc[period]["minimum"]
-    input_max = state_ranges[input_factor].loc[period]["maximum"]
+    period_data = states_data.query(f"aug_period == {aug_period}")[all_factors]
+    input_min = state_ranges[input_factor].loc[aug_period]["minimum"]
+    input_max = state_ranges[input_factor].loc[aug_period]["maximum"]
     to_concat = []
     for quantile in quantiles_of_other_factors:
         input_data = pd.DataFrame()
@@ -475,30 +477,21 @@ def _process_quantiles_of_other_factors(quantiles_of_other_factors):
 def _prepare_data_for_one_plot_average_2d(
     states_data,
     state_ranges,
-    period,
+    aug_period,
     input_factor,
     output_factor,
-    model,
     n_points,
     n_draws,
     transition_function,
     transition_params,
     all_factors,
 ):
-    if model["endogenous_factors_info"]["has_endogenous_factors"]:
-        aug_periods = [
-            aug_p
-            for aug_p, p in model["labels"]["aug_periods_to_periods"].items()
-            if p == period
-        ]
-    else:
-        aug_periods = [period]
-    period_data = states_data.query(f"period in {aug_periods}")
+    period_data = states_data.query(f"aug_period == {aug_period}")
 
     sampled_factors = [factor for factor in all_factors if factor != input_factor]
     draws = period_data[sampled_factors].sample(n=n_draws)
-    input_min = state_ranges[input_factor].loc[period]["minimum"]
-    input_max = state_ranges[input_factor].loc[period]["maximum"]
+    input_min = state_ranges[input_factor].loc[aug_period]["minimum"]
+    input_max = state_ranges[input_factor].loc[aug_period]["maximum"]
 
     to_concat = []
     for _, draw in draws.iterrows():

From 3bfdd0426fc777e9666c5ab1d20a6cd736dc140c Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Sun, 12 Oct 2025 12:00:45 +0200
Subject: [PATCH 44/48] Do not include correction factors as dependend
 variables in transition plots by default.

---
 src/skillmodels/visualize_transition_equations.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index d6eef74..e4fa854 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -141,6 +141,7 @@ def get_transition_plots(
     n_draws=50,
     colorscale="Magenta_r",
     layout_kwargs=None,
+    include_correction_factors=False,
 ):
     """Get dictionary with individual plots of transition equations for each factor.
 
@@ -163,6 +164,8 @@ def get_transition_plots(
         layout_kwargs (dict or NoneType): Dictionary of key word arguments used to
             update layout of plotly image object. If None, the default kwargs
             defined in the function will be used.
+        include_correction_factors (bool): Whether to include correction factors in the
+            plots. Default False.
 
     Returns:
         plots_dict (dict): Dictionary with individual plots of transition equations
@@ -180,7 +183,14 @@ def get_transition_plots(
             "*period* must be the penultimate period of the model or earlier.",
         )
 
-    latent_factors = model["labels"]["latent_factors"]
+    if include_correction_factors:
+        latent_factors = model["labels"]["latent_factors"]
+    else:
+        latent_factors = [
+            lf
+            for lf in model["labels"]["latent_factors"]
+            if not model["endogenous_factors_info"][lf]["is_correction"]
+        ]
     all_factors = model["labels"]["all_factors"]
     states = get_filtered_states(model_dict=model_dict, data=data, params=params)[
         "anchored_states"
@@ -237,7 +247,7 @@ def _get_dictionary_with_plots(
 
         latent_factors (list): Latent factors of the model that are outputs of
             transition factors.
-        all_factors (list): All factors of the model that are the inuts of transition
+        all_factors (list): All factors of the model that are the inputs of transition
             functions.
         quantiles_of_other_factors (float, list or None): Quantiles at which the factors
             that are not varied in a given plot are fixed. If None, those factors are

From c45cb47b68b62716012b83d230f26e24f9aa4ed9 Mon Sep 17 00:00:00 2001
From: "Hans-Martin v. Gaudecker" <hmgaudecker@gmail.com>
Date: Sun, 12 Oct 2025 13:07:43 +0200
Subject: [PATCH 45/48] Fix tests, basically allowing non-augmented models
 again.

---
 src/skillmodels/constraints.py                | 10 ++--
 src/skillmodels/process_data.py               |  1 +
 .../visualize_transition_equations.py         | 18 +++++--
 tests/test_constraints.py                     | 48 +++++++++----------
 tests/test_visualize_factor_distributions.py  |  2 +-
 5 files changed, 46 insertions(+), 33 deletions(-)

diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index ab57e1d..1decb85 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -212,18 +212,18 @@ def _get_stage_constraints(stagemap, stages) -> list[dict]:
 
     for stage, stage_periods in stages_to_periods.items():
         if len(stage_periods) > 1:
-            locs_trans = [("transition", p) for p in stage_periods]
-            locs_q = [("shock_sds", p) for p in stage_periods]
+            loc_trans = [("transition", p) for p in stage_periods]
+            loc_q = [("shock_sds", p) for p in stage_periods]
             constraints_dicts.append(
                 {
-                    "locs": locs_trans,
+                    "loc": loc_trans,
                     "type": "pairwise_equality",
                     "description": msg.format(stage),
                 },
             )
             constraints_dicts.append(
                 {
-                    "locs": locs_q,
+                    "loc": loc_q,
                     "type": "pairwise_equality",
                     "description": msg.format(stage),
                 },
@@ -526,7 +526,7 @@ def constraints_dicts_to_om(
         if c_d["type"] == "pairwise_equality":
             om_style.append(
                 SkillmodelsPairwiseEqualityConstraint(
-                    selectors=[functools.partial(_sel, loc=loc) for loc in c_d["locs"]],
+                    selectors=[functools.partial(_sel, loc=loc) for loc in c_d["loc"]],
                     **c_d,
                 )
             )
diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 6450f71..25d9f39 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -47,6 +47,7 @@ def process_data(
         df = _augment_data_for_endogenous_factors(df, labels, update_info)
     else:
         df = _add_copies_of_anchoring_outcome(df, anchoring_info)
+        df.index = df.index.set_names(["id", "aug_period"])
 
     _check_data(df, update_info, labels, purpose=purpose)
     n_obs = int(len(df) / len(labels["aug_periods"]))
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index e4fa854..4dc5304 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -183,7 +183,10 @@ def get_transition_plots(
             "*period* must be the penultimate period of the model or earlier.",
         )
 
-    if include_correction_factors:
+    if (
+        include_correction_factors
+        or not model["endogenous_factors_info"]["has_endogenous_factors"]
+    ):
         latent_factors = model["labels"]["latent_factors"]
     else:
         latent_factors = [
@@ -278,13 +281,22 @@ def _get_dictionary_with_plots(
         title_kwargs=None,
         showlegend=showlegend,
     )
-    _aug_periods = model["endogenous_factors_info"]["aug_periods_from_period"](period)
+    has_endogenous_factors = model["endogenous_factors_info"]["has_endogenous_factors"]
+    if has_endogenous_factors:
+        _aug_periods = model["endogenous_factors_info"]["aug_periods_from_period"](
+            period
+        )
+    else:
+        _aug_periods = [period]
     plots_dict = {}
     for output_factor, input_factor in itertools.product(latent_factors, all_factors):
         transition_function = model["transition_info"]["individual_functions"][
             output_factor
         ]
-        if model["endogenous_factors_info"][output_factor]["is_endogenous"]:
+        if (
+            has_endogenous_factors
+            and model["endogenous_factors_info"][output_factor]["is_endogenous"]
+        ):
             aug_period = min(_aug_periods)
         else:
             aug_period = max(_aug_periods)
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index 9d5e449..ca3bb10 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -123,11 +123,11 @@ def test_stage_constraints():
 
     expected = [
         {
-            "locs": [("transition", 0), ("transition", 1), ("transition", 2)],
+            "loc": [("transition", 0), ("transition", 1), ("transition", 2)],
             "type": "pairwise_equality",
         },
         {
-            "locs": [("shock_sds", 0), ("shock_sds", 1), ("shock_sds", 2)],
+            "loc": [("shock_sds", 0), ("shock_sds", 1), ("shock_sds", 2)],
             "type": "pairwise_equality",
         },
     ]
@@ -143,19 +143,19 @@ def test_stage_constraints_with_endogenous_factors():
     stagemap = [0, 1, 0, 1, 2, 3]
     expected = [
         {
-            "locs": [("transition", 0), ("transition", 2)],
+            "loc": [("transition", 0), ("transition", 2)],
             "type": "pairwise_equality",
         },
         {
-            "locs": [("transition", 1), ("transition", 3)],
+            "loc": [("transition", 1), ("transition", 3)],
             "type": "pairwise_equality",
         },
         {
-            "locs": [("shock_sds", 0), ("shock_sds", 2)],
+            "loc": [("shock_sds", 0), ("shock_sds", 2)],
             "type": "pairwise_equality",
         },
         {
-            "locs": [("shock_sds", 1), ("shock_sds", 3)],
+            "loc": [("shock_sds", 1), ("shock_sds", 3)],
             "type": "pairwise_equality",
         },
     ]
@@ -384,23 +384,23 @@ def test_get_constraints_for_augmented_periods(simplest_augmented_model):
     for c in calculated:
         del c["description"]
     expected = [
-        {"loc": ("transition", 1, "fac1", "fac1"), "type": "fixed", "value": 1.0},
-        {"loc": ("transition", 1, "fac1", "fac2"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 1, "fac1", "of"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 1, "fac1", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("shock_sds", 1, "fac1", "-"), "type": "fixed", "value": 0.00000001},
-        {"loc": ("transition", 3, "fac1", "fac1"), "type": "fixed", "value": 1.0},
-        {"loc": ("transition", 3, "fac1", "fac2"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 3, "fac1", "of"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 3, "fac1", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 0, "fac2", "fac1"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 0, "fac2", "fac2"), "type": "fixed", "value": 1.0},
-        {"loc": ("transition", 0, "fac2", "of"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 0, "fac2", "constant"), "type": "fixed", "value": 0.0},
-        {"loc": ("shock_sds", 0, "fac2", "-"), "type": "fixed", "value": 0.00000001},
-        {"loc": ("transition", 2, "fac2", "fac1"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 2, "fac2", "fac2"), "type": "fixed", "value": 1.0},
-        {"loc": ("transition", 2, "fac2", "of"), "type": "fixed", "value": 0.0},
-        {"loc": ("transition", 2, "fac2", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 0, "fac1", "fac1"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 0, "fac1", "fac2"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 0, "fac1", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 0, "fac1", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 0, "fac1", "-"), "type": "fixed", "value": 0.00000001},
+        {"loc": ("transition", 2, "fac1", "fac1"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 2, "fac1", "fac2"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 2, "fac1", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 2, "fac1", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 1, "fac2", "fac1"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 1, "fac2", "fac2"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 1, "fac2", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 1, "fac2", "constant"), "type": "fixed", "value": 0.0},
+        {"loc": ("shock_sds", 1, "fac2", "-"), "type": "fixed", "value": 0.00000001},
+        {"loc": ("transition", 3, "fac2", "fac1"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 3, "fac2", "fac2"), "type": "fixed", "value": 1.0},
+        {"loc": ("transition", 3, "fac2", "of"), "type": "fixed", "value": 0.0},
+        {"loc": ("transition", 3, "fac2", "constant"), "type": "fixed", "value": 0.0},
     ]
     assert_list_equal_except_for_order(calculated, expected)
diff --git a/tests/test_visualize_factor_distributions.py b/tests/test_visualize_factor_distributions.py
index f048384..1895301 100644
--- a/tests/test_visualize_factor_distributions.py
+++ b/tests/test_visualize_factor_distributions.py
@@ -67,7 +67,7 @@ def test_visualize_factor_distributions_runs_with_simulated_states():
     params = params.loc[max_inputs["params_template"].index]
 
     latent_data = simulate_dataset(model_dict, params, data=data, policies=None)[
-        "unanchored_states"
+        "aug_unanchored_states"
     ]["states"]
 
     kde = univariate_densities(

From 0d673b708ab67f70727790677b9050e1b5a512ba Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Sun, 12 Oct 2025 13:14:25 +0200
Subject: [PATCH 46/48] Update actions/pyproject.

---
 .github/workflows/main.yml |   46 +-
 pixi.lock                  | 9752 ++++++++++++++++++++----------------
 pyproject.toml             |    4 +-
 3 files changed, 5319 insertions(+), 4483 deletions(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 97fc397..2f61c8f 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -25,49 +25,23 @@ jobs:
         python-version:
           - '3.13'
     steps:
-      - uses: actions/checkout@v4
-      - uses: prefix-dev/setup-pixi@v0.8.10
+      - uses: actions/checkout@v5
+      - uses: prefix-dev/setup-pixi@v0.9.1
         with:
-          pixi-version: v0.49.0
+          pixi-version: v0.56.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: test-cpu
           activate-environment: true
+          frozen: true
       - name: Run pytest
+        if: runner.os != 'Linux' || matrix.python-version != '3.13'
+        shell: bash -l {0}
+        run: pixi run -e test-cpu tests
+      - name: Run pytest with coverage
+        if: runner.os == 'Linux' && matrix.python-version == '3.13'
         shell: bash -l {0}
         run: pixi run -e test-cpu tests-with-cov
       - name: Upload coverage report
         if: runner.os == 'Linux' && matrix.python-version == '3.13'
-        uses: codecov/codecov-action@v4
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-  # run-mypy:
-  #   name: Run mypy on Python 3.13
-  #   runs-on: ubuntu-latest
-  #   strategy:
-  #     fail-fast: false
-  #   steps:
-  #     - uses: actions/checkout@v4
-  #     - uses: prefix-dev/setup-pixi@v0.8.3
-  #       with:
-  #         pixi-version: v0.28.2
-  #         cache: true
-  #         cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
-  #         environments: mypy
-  #     - name: Run mypy
-  #       shell: bash -l {0}
-  #       run: pixi run mypy
-  # run-explanation-notebooks:
-  #   name: Run explanation notebooks on Python 3.13
-  #   runs-on: ubuntu-latest
-  #   steps:
-  #     - uses: actions/checkout@v4
-  #     - uses: prefix-dev/setup-pixi@v0.8.3
-  #       with:
-  #         pixi-version: v0.28.2
-  #         cache: true
-  #         cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
-  #         environments: test
-  #     - name: Run explanation notebooks
-  #       shell: bash -l {0}
-  #       run: pixi run -e test explanation-notebooks
+        uses: codecov/codecov-action@v5
diff --git a/pixi.lock b/pixi.lock
index 582516d..f5f6303 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,245 +9,254 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py313h536fd9c_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.11.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.5-pyh29332c3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.3.0-pyh71513ae_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyh71513ae_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.3-pyha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.2-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.44-hdf8817f_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.44-h4852527_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.2.0-pyh29332c3_4.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.2.0-h82add2a_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py313h46c70d0_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb03c661_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb03c661_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py314ha160325_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.10.5-hbd8a1cb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.1.31-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py313hfab6e84_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.10.5-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314ha6a4811_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/choreographer-1.1.1-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py313h33d0bda_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.93-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.93-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.8.90-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.8.90-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.8.90-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-static-12.8.90-h5888daf_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-static_linux-64-12.8.90-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart_linux-64-12.8.90-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-driver-dev_linux-64-12.8.90-h3f2d84a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-12.8.93-hcdd1206_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvcc-dev_linux-64-12.8.93-he91c749_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-impl-12.8.93-h85509e4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-tools-12.8.93-he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc_linux-64-12.8.93-h04802cd_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-nvvm-dev_linux-64-12.8.93-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-impl-12.8.93-he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.8.93-he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.8-h5d125a7_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.3-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/choreographer-1.1.2-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py314h9891dd4_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.0-py314hd8ed1ab_100.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.9.27-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.9.86-ha770c72_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.9.86-ha770c72_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.9.79-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.9.79-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.9.79-h3f2d84a_0.conda
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+  - pkg:pypi/jupyter-events?source=hash-mapping
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-  - python >=3.9
+  - python >=3.10
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-  - python >=3.9
-  - setuptools >=40.8.0
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-  license: LGPL-2.0-or-later AND LGPL-2.0-or-later WITH exceptions AND GPL-2.0-or-later AND MPL-2.0
+  - sysroot_linux-64 ==2.28
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+  - python
+  - vc >=14.3,<15
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+  - vc >=14.3,<15
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   license: BSD-3-Clause
   license_family: BSD
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-  size: 55591
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+  md5: c9ee16acbcea5cc91d9f3eb1d8f903bd
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   - lzo >=2.10,<3.0a0
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+  - openssl >=3.5.2,<4.0a0
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   license: BSD-2-Clause
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   - mkl <2025
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-  - libcblas =3.9.0=31*_openblas
-  - blas =2.131=openblas
+  - liblapacke 3.9.0   37*_openblas
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   - mkl <2025
-  - liblapack =3.9.0=31*_openblas
+  - liblapack  3.9.0   37*_openblas
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-  - blas =2.131=mkl
-  - liblapacke =3.9.0=31*_mkl
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   license: MIT AND BSD-3-Clause-Clear
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+  - intel-openmp <0.0a0
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   - xorg-libxcursor >=1.2.3,<2.0a0
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   - xorg-libxxf86vm >=1.1.6,<2.0a0
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+  - zstd >=1.5.7,<1.6.0a0
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+  - qt 6.9.3
   license: LGPL-3.0-only
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+  size: 54537863
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-  - libtiff >=4.7.0,<4.8.0a0
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   - libzlib >=1.3.1,<2.0a0
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+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - zstd >=1.5.7,<1.6.0a0
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+  - qt 6.9.3
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 94138960
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+  size: 96316397
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda
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-- conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda
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-  md5: a9b9368f3701a417eac9edbcae7cb737
+- conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.5-pyhd8ed1ab_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda
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+  md5: 7234f99325263a5af6d4cd195035e8f2
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+  - python
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.23.1-py313h6071e0b_0.conda
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+  - pkg:pypi/rich?source=compressed-mapping
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.27.1-py313h843e2db_1.conda
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+  - libgcc >=14
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+  - numpy <2.6
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-  size: 32581
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   timestamp: 1733236131358
-- conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.8.0-pyhd8ed1ab_1.conda
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-  size: 46718
-  timestamp: 1733157432924
+  - pkg:pypi/websocket-client?source=compressed-mapping
+  size: 61391
+  timestamp: 1759928175142
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@@ -9777,17 +10601,18 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-hb711507_2.conda
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-  md5: 8637c3e5821654d0edf97e2b0404b443
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-h4f16b4b_2.conda
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+  - __glibc >=2.17,<3.0.a0
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+  - libxcb >=1.17.0,<2.0a0
   license: MIT
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   purls: []
-  size: 19965
-  timestamp: 1718843348208
+  size: 20772
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.5-hb9d3cd8_0.conda
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@@ -9848,18 +10673,18 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.43-hb9d3cd8_0.conda
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-  md5: f725c7425d6d7c15e31f3b99a88ea02f
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.46-hb03c661_0.conda
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-  - libgcc >=13
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+  - libgcc >=14
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   license: MIT
   license_family: MIT
   purls: []
-  size: 389475
-  timestamp: 1727840188958
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+  timestamp: 1759543819846
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.1-hb9d3cd8_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.2-hb03c661_0.conda
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   license: MIT
   license_family: MIT
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-  size: 19575
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
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+  md5: 78a0fe9e9c50d2c381e8ee47e3ea437d
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-- conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h8ffe710_2.tar.bz2
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-  md5: adbfb9f45d1004a26763652246a33764
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+  timestamp: 1753484079473
+- conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda
+  sha256: 80ee68c1e7683a35295232ea79bcc87279d31ffeda04a1665efdb43cbd50a309
+  md5: 433699cba6602098ae8957a323da2664
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+  - ucrt >=10.0.20348.0
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+  - ucrt >=10.0.20348.0
   license: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda
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-  md5: 3947a35e916fcc6b9825449affbf4214
+  size: 63944
+  timestamp: 1753484092156
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h387f397_9.conda
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+  md5: 8035e5b54c08429354d5d64027041cad
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+  - libstdcxx >=14
+  - libgcc >=14
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-  - libgcc >=13
+  - libgcc >=14
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-  - libstdcxx >=13
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   license: MPL-2.0
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hc1bb282_7.conda
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   - libsodium >=1.0.20,<1.0.21.0a0
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   license: MPL-2.0
   license_family: MOZILLA
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-- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-ha9f60a1_7.conda
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-  - krb5 >=1.21.3,<1.22.0a0
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+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libsodium >=1.0.20,<1.0.21.0a0
+  - krb5 >=1.21.3,<1.22.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 2527503
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-- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
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+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda
+  sha256: 7560d21e1b021fd40b65bfb72f67945a3fcb83d78ad7ccf37b8b3165ec3b68ad
+  md5: df5e78d904988eb55042c0c97446079f
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   license: MIT
   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py313h536fd9c_1.conda
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+  size: 22963
+  timestamp: 1749421737203
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py313h54dd161_0.conda
+  sha256: 9d79d176afe50361cc3fd4366bedff20852dbea1e5b03f358b55f12aca22d60d
+  md5: 1fe43bd1fc86e22ad3eb0edec637f8a2
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+  - python
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+  - zstd >=1.5.7,<1.5.8.0a0
+  - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
+  - zstd >=1.5.7,<1.6.0a0
   - python_abi 3.13.* *_cp313
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 737893
-  timestamp: 1741853442447
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py313h90d716c_1.conda
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+  size: 471152
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py314h31f8a6b_0.conda
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-  - __osx >=11.0
+  - python
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-  - python >=3.13,<3.14.0a0
-  - python >=3.13,<3.14.0a0 *_cp313
-  - python_abi 3.13.* *_cp313
+  - zstd >=1.5.7,<1.5.8.0a0
+  - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
+  - zstd >=1.5.7,<1.6.0a0
+  - python_abi 3.14.* *_cp314
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 127864
+  timestamp: 1757930108791
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.25.0-py314h163e31d_0.conda
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+  md5: c824d8cd887ce1d7af8963ca4087a764
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+  - python
+  - cffi >=1.11
+  - zstd >=1.5.7,<1.5.8.0a0
+  - __osx >=11.0
+  - python 3.14.* *_cp314
+  - zstd >=1.5.7,<1.6.0a0
+  - python_abi 3.14.* *_cp314
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 536091
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-- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313ha7868ed_1.conda
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-  md5: 0fe8f97370e74acbc7814c4906a5824f
+  size: 125883
+  timestamp: 1757930173407
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.25.0-py314h4667ab5_0.conda
+  sha256: 285890e987cb14b2ac27f74a4ae844eb80e73654ab3321f085e13538c6ac7d23
+  md5: 765406da1e9f31231a56ea4d2e191d7c
   depends:
+  - python
   - cffi >=1.11
-  - python >=3.13,<3.14.0a0
-  - python_abi 3.13.* *_cp313
+  - zstd >=1.5.7,<1.5.8.0a0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - zstd >=1.5.7,<1.6.0a0
+  - python_abi 3.14.* *_cp314
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 449910
-  timestamp: 1741853538921
+  size: 285971
+  timestamp: 1757930137171
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
   sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
   md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
diff --git a/pyproject.toml b/pyproject.toml
index 024bf35..27362f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,9 +12,10 @@ maintainers = [
     { name = "Hans-Martin von Gaudecker", email = "hmgaudecker@uni-bonn.de" },
 ]
 description = "Estimators for skill formation models"
-requires-python = ">=3.12"
+requires-python = ">=3.13"
 dependencies = [
     "dags",
+    "jax>=0.7",
     "numpy",
     "pandas",
 ]
@@ -105,7 +106,6 @@ scipy = "*"
 
 [tool.pixi.pypi-dependencies]
 skillmodels = {path = ".", editable = true}
-jax = ">=0.7"
 optimagic = { git = "https://github.com/optimagic-dev/optimagic.git", branch = "main"}
 pdbp = "*"
 

From 6ed253d6cd13885a273bef4d3e5c7eed33c2d125 Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 13 Oct 2025 05:24:39 +0200
Subject: [PATCH 47/48] Use codecov token again.

---
 .github/workflows/main.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 2f61c8f..dbf570f 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -45,3 +45,5 @@ jobs:
       - name: Upload coverage report
         if: runner.os == 'Linux' && matrix.python-version == '3.13'
         uses: codecov/codecov-action@v5
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

From cc710a63eba54028f7da91875a5316700704d85d Mon Sep 17 00:00:00 2001
From: Hans-Martin von Gaudecker <hmgaudecker@gmail.com>
Date: Mon, 13 Oct 2025 06:01:38 +0200
Subject: [PATCH 48/48] Fix docstring.

---
 src/skillmodels/process_data.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/skillmodels/process_data.py b/src/skillmodels/process_data.py
index 25d9f39..3b6407a 100644
--- a/src/skillmodels/process_data.py
+++ b/src/skillmodels/process_data.py
@@ -29,14 +29,14 @@ def process_data(
             estimation (default, includes measurement data) or not.
 
     Returns a dictionary with keys:
-        controls (jax.numpy.array): Array of shape (n_updates, n_obs) with data on
+        measurements (jax.numpy.array): Array of shape (n_updates, n_obs) with data on
             observed measurements. NaN if the measurement was not observed. Only
             returned if estimation==True
+        controls (jax.numpy.array): Array of shape (n_periods, n_obs, n_controls) with
+            observed control variables for the measurement equations.
         observed_factors (jax.numpy.array): Array of shape
-            (n_periods, n_obs, n_controls) with observed control variables for the
-            measurement equations.
-        measurements (jax.numpy.array): Array of shape (n_periods, n_obs,
-            n_observed_factors) with data on the observed factors.
+            (n_periods, n_obs, n_observed_factors) with data on the observed factors.
+            Only returned if estimation==True
 
     """
     df = pre_process_data(df, labels["periods"])