diff --git a/.ai-instructions b/.ai-instructions index 68dc3f48..0ebfc05c 160000 --- a/.ai-instructions +++ b/.ai-instructions @@ -1 +1 @@ -Subproject commit 68dc3f4867cb075b0e0d8eadf383800ea33f5995 +Subproject commit 0ebfc05c325d42dacaa8caf10053997db45a4c57 diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 8805f374..9d029ce6 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -28,7 +28,7 @@ jobs: - uses: actions/checkout@v7 - uses: prefix-dev/setup-pixi@v0.10.0 with: - pixi-version: v0.71.2 + pixi-version: v0.74.0 cache: true cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }} environments: tests-cpu @@ -54,7 +54,7 @@ jobs: - uses: actions/checkout@v7 - uses: prefix-dev/setup-pixi@v0.10.0 with: - pixi-version: v0.71.2 + pixi-version: v0.74.0 cache: true cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }} frozen: true diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index abcfa728..62cd840f 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -16,7 +16,7 @@ jobs: # (including tags) must be fetched. fetch-depth: 0 - name: Set up Python 3.12 - uses: actions/setup-python@v6 + uses: actions/setup-python@v7 with: python-version: '3.12' - name: Install pypa/build diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d04fc99b..7303ed17 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,7 +5,7 @@ repos: - id: check-hooks-apply - id: check-useless-excludes - repo: https://github.com/tox-dev/pyproject-fmt - rev: v2.25.1 + rev: v2.26.0 hooks: - id: pyproject-fmt - repo: https://github.com/lyz-code/yamlfix @@ -46,8 +46,12 @@ repos: rev: v1.38.0 hooks: - id: yamllint + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: 0.37.4 + hooks: + - id: check-github-workflows - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.15.20 + rev: v0.16.0 hooks: - id: ruff-check args: @@ -62,7 +66,7 @@ repos: - pyi - python - repo: https://github.com/astral-sh/ty-pre-commit - rev: v0.0.55 + rev: v0.0.64 hooks: - id: ty # `--no-project` stops uv from creating a `.venv`/`uv.lock` in this @@ -79,7 +83,9 @@ repos: exclude: ^docs/getting_started/tutorial\.ipynb$ args: - --extra-keys - - metadata.kernelspec metadata.language_info.version metadata.vscode + # metadata.kernelspec is kept: this project uses Jupyter Book 2 + # (mystmd), which needs kernelspec to select the execution kernel. + - metadata.language_info.version metadata.vscode - repo: https://github.com/executablebooks/mdformat rev: 1.0.0 hooks: @@ -92,6 +98,14 @@ repos: - --wrap - '88' files: (AGENTS\.md|CLAUDE\.md|README\.md|modules/.*\.md|profiles/.*\.md) + - id: mdformat + additional_dependencies: + - mdformat-myst + - mdformat-ruff + args: + - --wrap + - '88' + files: (docs/.*) ci: autoupdate_schedule: monthly # pre-commit.ci has no pixi environments and blocks network at hook runtime; diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index 6d1ede62..00000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,14 +0,0 @@ ---- -version: 2 -build: - os: ubuntu-24.04 - tools: - python: '3.12' - commands: - # install pixi - - curl -fsSL https://pixi.sh/install.sh | bash - # build the Jupyter Book 2 / MyST site using the project's docs env - - ~/.pixi/bin/pixi run -e docs build-docs - # publish the generated HTML where Read the Docs expects it - - mkdir -p "$READTHEDOCS_OUTPUT/html" - - cp -r docs/_build/html/. "$READTHEDOCS_OUTPUT/html" diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 00000000..6b73b8de --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,269 @@ +@.ai-instructions/profiles/tier-a.md @.ai-instructions/modules/jax.md +@.ai-instructions/modules/optimagic.md @.ai-instructions/modules/pandas.md +@.ai-instructions/modules/plotting.md + +# skillmodels + +This file provides guidance to AI coding agents when working with code in this +repository. + +## Project Overview + +skillmodels is a Python implementation of estimators for nonlinear dynamic latent factor +models, primarily used for skill formation research in economics. It implements Kalman +filter-based maximum likelihood estimation following Cunha, Heckman, Schennach (2010). + +Used as the core estimation engine by sibling application projects (`skane-struct-bw`, +`health-cognition`) in the parent workspace. + +## Development Commands + +```bash +# Run tests +pixi run -e tests-cpu tests + +# Run tests with coverage +pixi run -e tests-cpu tests-with-cov + +# Run a single test file +pixi run -e tests-cpu pytest tests/test_kalman_filters.py + +# Run a single test +pixi run -e tests-cpu pytest tests/test_kalman_filters.py::test_function_name + +# Type checking (ty runs as a pre-commit hook, not a pixi task) +prek run ty --all-files + +# Quality checks (linting, formatting, type checking) +prek run --all-files + +# Build documentation (mystmd, from docs/ directory) +myst build +``` + +## Command Rules + +Always use these command mappings: + +- **Python**: Use `pixi run python` instead of `python` or `python3` +- **Type checker**: ty runs as a pre-commit hook (`prek run ty --all-files`), not as a + pixi task — it resolves imports from the env in `[tool.ty] environment.python` +- **Tests**: Use `pixi run -e tests-cpu tests` instead of `pytest` directly +- **Linting/formatting**: Use `prek run --all-files` instead of `ruff` directly +- **All quality checks**: Use `prek run --all-files` + +Before finishing any task that modifies code, always run: + +1. `pixi run -e tests-cpu tests` (tests) +1. `prek run --all-files` (quality checks, including the ty pre-commit hook) + +## Architecture + +### Core Pipeline Flow + +``` +ModelSpec + Data + ↓ +process_model() → Validates/extends model specification → ProcessedModel + ↓ +get_maximization_inputs() → Creates optimization problem (likelihood, gradients, + constraints, params_template) + ↓ +[optimagic maximize / estimagic estimate_ml with fides algorithm] + ↓ +get_filtered_states() → Extract estimated latent factors +simulate_dataset() → Simulate states (with optional policy effects) +``` + +### Key Modules + +- **model_spec.py**: User-facing frozen dataclasses (`ModelSpec`, `FactorSpec`, + `AnchoringSpec`). Re-exports `EstimationOptions` and `Normalizations` from `types.py`. + `ModelSpec` supports construction via `__init__` or `ModelSpec.from_dict()`, and + fluent builder methods: `with_transition_functions()`, `with_added_factor()`, + `with_added_observed_factors()`, `with_estimation_options()`, `with_anchoring()`, + `with_controls()`, `with_stagemap()`. +- **types.py**: Internal frozen dataclasses (`ProcessedModel`, `Labels`, `Dimensions`, + `Anchoring`, `ParsingInfo`, `ParsedParams`, `EndogenousFactorsInfo`, etc.), + `EstimationOptions`, `Normalizations`, and immutability utilities. +- **process_model.py**: Model specification validation and preprocessing. Converts + `ModelSpec` into `ProcessedModel`. +- **kalman_filters.py**: Core Kalman filter implementation (predict/update steps). Uses + square-root form for numerical stability. +- **likelihood_function.py** / **likelihood_function_debug.py**: Log-likelihood + computation using Kalman filtering. The debug variant is not jitted and returns + intermediate results (residuals, contributions, filtered states). +- **constraints.py**: Generates parameter constraints (bounds, equalities from stagemap, + fixed values) for optimization. Exports `get_constraints()`, + `enforce_fixed_constraints()`, `add_bounds()`, `FixedConstraintWithValue`. +- **parse_params.py**: Converts flat parameter vectors to structured model parameters. + Exports `create_parsing_info()` and `parse_params()`. +- **params_index.py**: Builds the `pd.MultiIndex` for the params DataFrame via + `get_params_index()`. +- **transition_functions.py**: Pre-built transition equations: `linear`, `translog`, + `robust_translog`, `linear_and_squares`, `log_ces`, `log_ces_general`, `constant`. +- **decorators.py**: `register_params` decorator for custom transition functions. Tags a + callable with `__registered_params__` so skillmodels knows its parameter names. +- **process_data.py**: `process_data()` for internal estimation format, + `pre_process_data()` for reshaping data to long format with period indexing. +- **simulate_data.py**: `simulate_dataset()` and `simulate_policy_effect()`. +- **diagnostic_plots.py**: `plot_likelihood_contributions()` and + `plot_residual_boxplots()` (Plotly-based). +- **variance_decomposition.py**: `decompose_measurement_variance()` and + `summarize_measurement_reliability()`. +- **process_debug_data.py**: `create_state_ranges()` and `process_debug_data()` for + converting raw debug output into DataFrames. +- **utilities.py**: Model manipulation helpers (`extract_factors`, `remove_factors`, + `update_parameter_values`, `switch_translog_to_linear`, etc.). +- **Visualization modules** (not in `__all__`, imported by module path): + `correlation_heatmap.py`, `visualize_factor_distributions.py`, + `visualize_transition_equations.py`, `utils_plotting.py`. + +### `get_maximization_inputs()` Return Dict + +Returns a dict with 6 keys: + +- `"loglike"`: `(params: pd.DataFrame) -> float` — jitted scalar log-likelihood +- `"loglikeobs"`: `(params: pd.DataFrame) -> NDArray` — jitted per-observation + log-likelihood +- `"debug_loglike"`: `(params: pd.DataFrame) -> dict` — non-jitted, returns dict with + keys `value`, `contributions`, `residuals`, `residual_sds`, `filtered_states`, + `state_ranges`, etc. +- `"loglike_and_gradient"`: `(params: pd.DataFrame) -> tuple[float, NDArray]` +- `"constraints"`: list of optimagic constraint objects +- `"params_template"`: `pd.DataFrame` with correct MultiIndex and bounds; fixed + constraints pre-applied + +### ProcessedModel Key Attributes + +Applications frequently access these after calling `process_model(model_spec)`: + +- `processed.labels` — `.latent_factors`, `.observed_factors`, `.all_factors`, + `.controls`, `.stagemap`, `.stages`, `.aug_periods_to_periods` +- `processed.dimensions` — `.n_periods`, `.n_latent_factors` +- `processed.update_info` — DataFrame indexed by `(aug_period, variable)` with factor + columns and a `purpose` column +- `processed.endogenous_factors_info` — `.has_endogenous_factors`, + `.aug_periods_from_period(period)`, `.factor_info` +- `processed.normalizations` — dict of factor name to `Normalizations` +- `processed.transition_info` — `.func` (vectorized), `.individual_functions[factor]` + +### JAX Usage + +All computation-heavy code uses JAX for automatic differentiation and JIT compilation. +The codebase uses: + +- `jax.vmap` for vectorization across observations +- `jax.jit` for compilation +- JAX arrays throughout the estimation pipeline +- Optional GPU support via CUDA or Metal + +### Public API (`__init__.py`) + +**Model specification classes:** + +- `ModelSpec`, `FactorSpec`, `AnchoringSpec`, `EstimationOptions`, `Normalizations` + +**Core estimation:** + +- `get_maximization_inputs(model_spec, data, split_dataset=1)` — prepare optimization + problem +- `get_filtered_states(model_spec, data, params)` — returns nested dict with + `"anchored_states"` and `"unanchored_states"`, each containing `"states"` (DataFrame) + and `"state_ranges"` + +**Simulation:** + +- `simulate_dataset(model_spec, params, n_obs=None, data=None, policies=None, seed=None)` + — returns dict with `"unanchored_states"`, `"anchored_states"` +- `simulate_policy_effect(model_spec, params, data, policies, seed=None)` — returns + DataFrame of factor mean differences between policy and baseline + +**Diagnostics and visualization:** + +- `plot_likelihood_contributions(model_spec, data, params, period=None)` +- `plot_residual_boxplots(model_spec, data, params, period=None)` +- `decompose_measurement_variance(model_spec, params, data)` — returns DataFrame indexed + by `(period, measurement, factor)` with signal/noise columns +- `summarize_measurement_reliability(variance_decomposition)` +- `create_state_ranges(filtered_states, factors, quantile_cutoff=None)` + +### Frequently Used Internal APIs + +These are not in `__all__` but are imported directly by application projects: + +- `skillmodels.process_model.process_model` — central to all application code +- `skillmodels.types.ProcessedModel`, `EndogenousFactorsInfo` +- `skillmodels.decorators.register_params` — essential for custom transition functions +- `skillmodels.constraints.get_constraints`, `enforce_fixed_constraints`, + `FixedConstraintWithValue`, `select_by_loc` +- `skillmodels.utilities.extract_factors`, `update_parameter_values` +- `skillmodels.process_data.pre_process_data` +- `skillmodels.correlation_heatmap.get_measurements_corr`, `get_quasi_scores_corr`, + `get_scores_corr`, `plot_correlation_heatmap` +- `skillmodels.visualize_factor_distributions.univariate_densities`, + `bivariate_density_contours`, `combine_distribution_plots` +- `skillmodels.visualize_transition_equations.get_transition_plots`, + `combine_transition_plots` +- `skillmodels.parse_params.create_parsing_info`, `parse_params` +- `skillmodels.params_index.get_params_index` + +## Code Style + +- Require Python 3.14 +- Uses Ruff for linting (target: Python 3.14, line length: 88) +- Google-style docstrings with imperative mood ("Return" not "Returns") +- Use MyST syntax in docstrings (single backticks `like this`), not reStructuredText (no + double backticks, no `:ref:`, `:func:`, etc.) +- Dataclass attributes use inline docstrings (docstring on the line after the field): + ```python + name: str + """Description of name.""" + ``` +- Pre-commit hooks enforce formatting and linting +- Type checking via `ty` with strict rules +- Do not use `from __future__ import annotations` +- Use modern numpy random API: `rng = np.random.default_rng(seed)` instead of + `np.random.seed()` or legacy functions like `np.random.randn()` + +### Immutability Conventions + +- All model configuration and internal data structures use frozen dataclasses +- Dict fields on internal dataclasses use `MappingProxyType` (not `Mapping`); wrap at + the call site with `MappingProxyType(...)` +- Dict fields on user-facing dataclasses (`AnchoringSpec`, `Normalizations`) use + `Mapping` with `__post_init__` conversion via `ensure_containers_are_immutable()` +- List fields use `tuple`, set fields use `frozenset` +- `ensure_containers_are_immutable()` recursively converts dict→MappingProxyType, + list→tuple, set→frozenset + +### Period vs Aug_period + +Models with endogenous factors split each calendar period into multiple **augmented +periods** (`aug_period`). The public API uses `period` (user-facing); `aug_period` is +strictly internal. All public functions now return `period`: + +- `ModelSpec` — clean, no `aug_period` exposure. +- `get_transition_plots()` — clean, accepts `period`/`periods`. +- `get_filtered_states()` — clean, returns `period` column. +- `simulate_dataset()` — clean, returns `period` in states DataFrames. +- `plot_residual_boxplots()` / `plot_likelihood_contributions()` — clean, accept and + return `period`. +- `decompose_measurement_variance()` — clean, indexed by + `(period, measurement, factor)`. +- `simulate_policy_effect()` / `simulate_dataset()` policies — accept `"period"` key. +- `ProcessedModel.labels` — exposes `aug_periods_to_periods` mapping (acceptable for + internal/advanced use). + +When writing new public-facing code, always accept and return `period`. Convert to +`aug_period` internally using `ProcessedModel.labels.aug_periods_to_periods`. + +## Testing + +- pytest with markers: `wip`, `unit`, `integration`, `end_to_end`, `long_running` +- Test files mirror source structure in `tests/` +- Memory profiling available via pytest-memray (Unix only) +- MATLAB AF CES / translog reproduction tests live in the parent workspace at + `../matlab_ces_repro/` (alongside `sim_repro/`), not in this library. They depend on + reference data at `/home/hmg/sciebo/Skill estimation/` and the CNLSY xls bundled + beside them. Run from the workspace root. diff --git a/CLAUDE.md b/CLAUDE.md index 07ae25b5..43c994c2 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,268 +1 @@ -@.ai-instructions/profiles/tier-a.md @.ai-instructions/modules/jax.md -@.ai-instructions/modules/optimagic.md - -# CLAUDE.md - -This file provides guidance to Claude Code (claude.ai/code) when working with code in -this repository. - -## Project Overview - -skillmodels is a Python implementation of estimators for nonlinear dynamic latent factor -models, primarily used for skill formation research in economics. It implements Kalman -filter-based maximum likelihood estimation following Cunha, Heckman, Schennach (2010). - -Used as the core estimation engine by sibling application projects (`skane-struct-bw`, -`health-cognition`) in the parent workspace. - -## Development Commands - -```bash -# Run tests -pixi run -e tests-cpu tests - -# Run tests with coverage -pixi run -e tests-cpu tests-with-cov - -# Run a single test file -pixi run -e tests-cpu pytest tests/test_kalman_filters.py - -# Run a single test -pixi run -e tests-cpu pytest tests/test_kalman_filters.py::test_function_name - -# Type checking (ty runs as a pre-commit hook, not a pixi task) -prek run ty --all-files - -# Quality checks (linting, formatting, type checking) -prek run --all-files - -# Build documentation (mystmd, from docs/ directory) -myst build -``` - -## Command Rules - -Always use these command mappings: - -- **Python**: Use `pixi run python` instead of `python` or `python3` -- **Type checker**: ty runs as a pre-commit hook (`prek run ty --all-files`), not as a - pixi task — it resolves imports from the env in `[tool.ty] environment.python` -- **Tests**: Use `pixi run -e tests-cpu tests` instead of `pytest` directly -- **Linting/formatting**: Use `prek run --all-files` instead of `ruff` directly -- **All quality checks**: Use `prek run --all-files` - -Before finishing any task that modifies code, always run: - -1. `pixi run -e tests-cpu tests` (tests) -1. `prek run --all-files` (quality checks, including the ty pre-commit hook) - -## Architecture - -### Core Pipeline Flow - -``` -ModelSpec + Data - ↓ -process_model() → Validates/extends model specification → ProcessedModel - ↓ -get_maximization_inputs() → Creates optimization problem (likelihood, gradients, - constraints, params_template) - ↓ -[optimagic maximize / estimagic estimate_ml with fides algorithm] - ↓ -get_filtered_states() → Extract estimated latent factors -simulate_dataset() → Simulate states (with optional policy effects) -``` - -### Key Modules - -- **model_spec.py**: User-facing frozen dataclasses (`ModelSpec`, `FactorSpec`, - `AnchoringSpec`). Re-exports `EstimationOptions` and `Normalizations` from `types.py`. - `ModelSpec` supports construction via `__init__` or `ModelSpec.from_dict()`, and - fluent builder methods: `with_transition_functions()`, `with_added_factor()`, - `with_added_observed_factors()`, `with_estimation_options()`, `with_anchoring()`, - `with_controls()`, `with_stagemap()`. -- **types.py**: Internal frozen dataclasses (`ProcessedModel`, `Labels`, `Dimensions`, - `Anchoring`, `ParsingInfo`, `ParsedParams`, `EndogenousFactorsInfo`, etc.), - `EstimationOptions`, `Normalizations`, and immutability utilities. -- **process_model.py**: Model specification validation and preprocessing. Converts - `ModelSpec` into `ProcessedModel`. -- **kalman_filters.py**: Core Kalman filter implementation (predict/update steps). Uses - square-root form for numerical stability. -- **likelihood_function.py** / **likelihood_function_debug.py**: Log-likelihood - computation using Kalman filtering. The debug variant is not jitted and returns - intermediate results (residuals, contributions, filtered states). -- **constraints.py**: Generates parameter constraints (bounds, equalities from stagemap, - fixed values) for optimization. Exports `get_constraints()`, - `enforce_fixed_constraints()`, `add_bounds()`, `FixedConstraintWithValue`. -- **parse_params.py**: Converts flat parameter vectors to structured model parameters. - Exports `create_parsing_info()` and `parse_params()`. -- **params_index.py**: Builds the `pd.MultiIndex` for the params DataFrame via - `get_params_index()`. -- **transition_functions.py**: Pre-built transition equations: `linear`, `translog`, - `robust_translog`, `linear_and_squares`, `log_ces`, `log_ces_general`, `constant`. -- **decorators.py**: `register_params` decorator for custom transition functions. Tags a - callable with `__registered_params__` so skillmodels knows its parameter names. -- **process_data.py**: `process_data()` for internal estimation format, - `pre_process_data()` for reshaping data to long format with period indexing. -- **simulate_data.py**: `simulate_dataset()` and `simulate_policy_effect()`. -- **diagnostic_plots.py**: `plot_likelihood_contributions()` and - `plot_residual_boxplots()` (Plotly-based). -- **variance_decomposition.py**: `decompose_measurement_variance()` and - `summarize_measurement_reliability()`. -- **process_debug_data.py**: `create_state_ranges()` and `process_debug_data()` for - converting raw debug output into DataFrames. -- **utilities.py**: Model manipulation helpers (`extract_factors`, `remove_factors`, - `update_parameter_values`, `switch_translog_to_linear`, etc.). -- **Visualization modules** (not in `__all__`, imported by module path): - `correlation_heatmap.py`, `visualize_factor_distributions.py`, - `visualize_transition_equations.py`, `utils_plotting.py`. - -### `get_maximization_inputs()` Return Dict - -Returns a dict with 6 keys: - -- `"loglike"`: `(params: pd.DataFrame) -> float` — jitted scalar log-likelihood -- `"loglikeobs"`: `(params: pd.DataFrame) -> NDArray` — jitted per-observation - log-likelihood -- `"debug_loglike"`: `(params: pd.DataFrame) -> dict` — non-jitted, returns dict with - keys `value`, `contributions`, `residuals`, `residual_sds`, `filtered_states`, - `state_ranges`, etc. -- `"loglike_and_gradient"`: `(params: pd.DataFrame) -> tuple[float, NDArray]` -- `"constraints"`: list of optimagic constraint objects -- `"params_template"`: `pd.DataFrame` with correct MultiIndex and bounds; fixed - constraints pre-applied - -### ProcessedModel Key Attributes - -Applications frequently access these after calling `process_model(model_spec)`: - -- `processed.labels` — `.latent_factors`, `.observed_factors`, `.all_factors`, - `.controls`, `.stagemap`, `.stages`, `.aug_periods_to_periods` -- `processed.dimensions` — `.n_periods`, `.n_latent_factors` -- `processed.update_info` — DataFrame indexed by `(aug_period, variable)` with factor - columns and a `purpose` column -- `processed.endogenous_factors_info` — `.has_endogenous_factors`, - `.aug_periods_from_period(period)`, `.factor_info` -- `processed.normalizations` — dict of factor name to `Normalizations` -- `processed.transition_info` — `.func` (vectorized), `.individual_functions[factor]` - -### JAX Usage - -All computation-heavy code uses JAX for automatic differentiation and JIT compilation. -The codebase uses: - -- `jax.vmap` for vectorization across observations -- `jax.jit` for compilation -- JAX arrays throughout the estimation pipeline -- Optional GPU support via CUDA or Metal - -### Public API (`__init__.py`) - -**Model specification classes:** - -- `ModelSpec`, `FactorSpec`, `AnchoringSpec`, `EstimationOptions`, `Normalizations` - -**Core estimation:** - -- `get_maximization_inputs(model_spec, data, split_dataset=1)` — prepare optimization - problem -- `get_filtered_states(model_spec, data, params)` — returns nested dict with - `"anchored_states"` and `"unanchored_states"`, each containing `"states"` (DataFrame) - and `"state_ranges"` - -**Simulation:** - -- `simulate_dataset(model_spec, params, n_obs=None, data=None, policies=None, seed=None)` - — returns dict with `"unanchored_states"`, `"anchored_states"` -- `simulate_policy_effect(model_spec, params, data, policies, seed=None)` — returns - DataFrame of factor mean differences between policy and baseline - -**Diagnostics and visualization:** - -- `plot_likelihood_contributions(model_spec, data, params, period=None)` -- `plot_residual_boxplots(model_spec, data, params, period=None)` -- `decompose_measurement_variance(model_spec, params, data)` — returns DataFrame indexed - by `(period, measurement, factor)` with signal/noise columns -- `summarize_measurement_reliability(variance_decomposition)` -- `create_state_ranges(filtered_states, factors, quantile_cutoff=None)` - -### Frequently Used Internal APIs - -These are not in `__all__` but are imported directly by application projects: - -- `skillmodels.process_model.process_model` — central to all application code -- `skillmodels.types.ProcessedModel`, `EndogenousFactorsInfo` -- `skillmodels.decorators.register_params` — essential for custom transition functions -- `skillmodels.constraints.get_constraints`, `enforce_fixed_constraints`, - `FixedConstraintWithValue`, `select_by_loc` -- `skillmodels.utilities.extract_factors`, `update_parameter_values` -- `skillmodels.process_data.pre_process_data` -- `skillmodels.correlation_heatmap.get_measurements_corr`, `get_quasi_scores_corr`, - `get_scores_corr`, `plot_correlation_heatmap` -- `skillmodels.visualize_factor_distributions.univariate_densities`, - `bivariate_density_contours`, `combine_distribution_plots` -- `skillmodels.visualize_transition_equations.get_transition_plots`, - `combine_transition_plots` -- `skillmodels.parse_params.create_parsing_info`, `parse_params` -- `skillmodels.params_index.get_params_index` - -## Code Style - -- Require Python 3.14 -- Uses Ruff for linting (target: Python 3.14, line length: 88) -- Google-style docstrings with imperative mood ("Return" not "Returns") -- Use MyST syntax in docstrings (single backticks `like this`), not reStructuredText (no - double backticks, no `:ref:`, `:func:`, etc.) -- Dataclass attributes use inline docstrings (docstring on the line after the field): - ```python - name: str - """Description of name.""" - ``` -- Pre-commit hooks enforce formatting and linting -- Type checking via `ty` with strict rules -- Do not use `from __future__ import annotations` -- Use modern numpy random API: `rng = np.random.default_rng(seed)` instead of - `np.random.seed()` or legacy functions like `np.random.randn()` - -### Immutability Conventions - -- All model configuration and internal data structures use frozen dataclasses -- Dict fields on internal dataclasses use `MappingProxyType` (not `Mapping`); wrap at - the call site with `MappingProxyType(...)` -- Dict fields on user-facing dataclasses (`AnchoringSpec`, `Normalizations`) use - `Mapping` with `__post_init__` conversion via `ensure_containers_are_immutable()` -- List fields use `tuple`, set fields use `frozenset` -- `ensure_containers_are_immutable()` recursively converts dict→MappingProxyType, - list→tuple, set→frozenset - -### Period vs Aug_period - -Models with endogenous factors split each calendar period into multiple **augmented -periods** (`aug_period`). The public API uses `period` (user-facing); `aug_period` is -strictly internal. All public functions now return `period`: - -- `ModelSpec` — clean, no `aug_period` exposure. -- `get_transition_plots()` — clean, accepts `period`/`periods`. -- `get_filtered_states()` — clean, returns `period` column. -- `simulate_dataset()` — clean, returns `period` in states DataFrames. -- `plot_residual_boxplots()` / `plot_likelihood_contributions()` — clean, accept and - return `period`. -- `decompose_measurement_variance()` — clean, indexed by - `(period, measurement, factor)`. -- `simulate_policy_effect()` / `simulate_dataset()` policies — accept `"period"` key. -- `ProcessedModel.labels` — exposes `aug_periods_to_periods` mapping (acceptable for - internal/advanced use). - -When writing new public-facing code, always accept and return `period`. Convert to -`aug_period` internally using `ProcessedModel.labels.aug_periods_to_periods`. - -## Testing - -- pytest with markers: `wip`, `unit`, `integration`, `end_to_end`, `long_running` -- Test files mirror source structure in `tests/` -- Memory profiling available via pytest-memray (Unix only) -- MATLAB AF CES / translog reproduction tests live in the parent workspace at - `../matlab_ces_repro/` (alongside `sim_repro/`), not in this library. They depend on - reference data at `/home/hmg/sciebo/Skill estimation/` and the CNLSY xls bundled - beside them. Run from the workspace root. +@AGENTS.md diff --git a/GEMINI.md b/GEMINI.md index 7dc4841d..43c994c2 100644 --- a/GEMINI.md +++ b/GEMINI.md @@ -1,2 +1 @@ -@.ai-instructions/profiles/tier-a.md @.ai-instructions/modules/jax.md -@.ai-instructions/modules/optimagic.md +@AGENTS.md diff --git a/codecov.yml b/codecov.yml index 0deb4005..af01bc0e 100644 --- a/codecov.yml +++ b/codecov.yml @@ -12,6 +12,6 @@ coverage: target: 80% project: default: - target: 87.5% + target: 95% ignore: - .tox/**/* diff --git a/docs/explanations/architecture.md b/docs/explanations/architecture.md index 7ef049be..8544337b 100644 --- a/docs/explanations/architecture.md +++ b/docs/explanations/architecture.md @@ -1,7 +1,7 @@ # Package Architecture -Skillmodels hosts three estimators under one model specification. The package -layout reflects that: +Skillmodels hosts three estimators under one model specification. The package layout +reflects that: ``` src/skillmodels/ @@ -61,45 +61,42 @@ src/skillmodels/ ## How the layers interact -Every estimator reads the same `ModelSpec` and produces the same canonical -params DataFrame (4-level MultiIndex -`(category, period, name1, name2)`). The differences live entirely below the -spec: +Every estimator reads the same `ModelSpec` and produces the same canonical params +DataFrame (4-level MultiIndex `(category, period, name1, name2)`). The differences live +entirely below the spec: -- **CHS** consumes `process_model(spec) -> ProcessedModel`, then plugs that - into the Kalman recursion. `CHSEstimationOptions` is passed at call time - to `get_maximization_inputs(spec, data, chs_options=...)`. -- **AF** also calls `process_model`, but uses `ProcessedModel` only for the - parameter index, labels, and transition info. The Kalman filter is not - invoked; period-specific Halton designs replace the predict step. -- **AMN** likewise calls `process_model` for the index/labels, then runs its - three-stage pipeline. The result re-uses the same params DataFrame format - so the AMN output can seed CHS or AF estimation when desired. +- **CHS** consumes `process_model(spec) -> ProcessedModel`, then plugs that into the + Kalman recursion. `CHSEstimationOptions` is passed at call time to + `get_maximization_inputs(spec, data, chs_options=...)`. +- **AF** also calls `process_model`, but uses `ProcessedModel` only for the parameter + index, labels, and transition info. The Kalman filter is not invoked; period-specific + Halton designs replace the predict step. +- **AMN** likewise calls `process_model` for the index/labels, then runs its three-stage + pipeline. The result re-uses the same params DataFrame format so the AMN output can + seed CHS or AF estimation when desired. -`process_model` itself is structural: it takes only the spec and produces -shapes, labels, transition info, and an `EndogenousFactorsInfo`. It does not -carry any estimator-specific tuning. Each estimator's options class -(`CHSEstimationOptions`, `AFEstimationOptions`, `AMNEstimationOptions`) is -passed in at call time. +`process_model` itself is structural: it takes only the spec and produces shapes, +labels, transition info, and an `EndogenousFactorsInfo`. It does not carry any +estimator-specific tuning. Each estimator's options class (`CHSEstimationOptions`, +`AFEstimationOptions`, `AMNEstimationOptions`) is passed in at call time. ## Why this split -The package grew organically: CHS was the original codebase; AF and AMN were -later additions. Earlier iterations stored CHS-only options on `ModelSpec`, -which made the spec leak CHS assumptions into a notionally agnostic container. -The split into `common/`, `chs/`, `af/`, `amn/` makes the scope of each piece -explicit at the import site: +The package grew organically: CHS was the original codebase; AF and AMN were later +additions. Earlier iterations stored CHS-only options on `ModelSpec`, which made the +spec leak CHS assumptions into a notionally agnostic container. The split into +`common/`, `chs/`, `af/`, `amn/` makes the scope of each piece explicit at the import +site: - `from skillmodels import ModelSpec` — pure structural description. -- `from skillmodels.chs import CHSEstimationOptions, get_maximization_inputs` - — CHS-specific. +- `from skillmodels.chs import CHSEstimationOptions, get_maximization_inputs` — + CHS-specific. - `from skillmodels.af import estimate_af, AFEstimationOptions` — AF-specific. - `from skillmodels.common.variance_decomposition import decompose_measurement_variance` — works for any estimator, given pre-computed filtered states. -The architectural principle: a function lives in `common/` iff it does not -import from `chs/`, `af/`, or `amn/`. Anything that does belongs in the -relevant subpackage. There is one practical exception: -`CHSEstimationOptions` is defined in `chs/options.py` but the -`process_model` orchestration in `common/` doesn't read it (it reads the -structural `ModelSpec.n_mixtures` field instead), so the layering is clean. +The architectural principle: a function lives in `common/` iff it does not import from +`chs/`, `af/`, or `amn/`. Anything that does belongs in the relevant subpackage. There +is one practical exception: `CHSEstimationOptions` is defined in `chs/options.py` but +the `process_model` orchestration in `common/` doesn't read it (it reads the structural +`ModelSpec.n_mixtures` field instead), so the layering is clean. diff --git a/docs/explanations/linear_predict.md b/docs/explanations/linear_predict.md index bd9de526..cc4984fc 100644 --- a/docs/explanations/linear_predict.md +++ b/docs/explanations/linear_predict.md @@ -9,23 +9,23 @@ back to the unscented predict. The check happens in `get_maximization_inputs`, where the predict function is selected via `functools.partial`. When the linear path is chosen, extra keyword arguments -(`latent_factors`, `constant_factor_indices`, `n_all_factors`) are bound at setup time so -the predict function has the same call signature as the unscented variant. +(`latent_factors`, `constant_factor_indices`, `n_all_factors`) are bound at setup time +so the predict function has the same call signature as the unscented variant. ## Why it is faster and uses less memory The unscented predict generates $2n + 1$ sigma points (where $n$ is the number of latent factors), transforms each one through the transition function, then recovers predicted -means and covariances from weighted statistics. Its QR decomposition operates on a matrix -of shape $(3n + 1) \times n$: the $2n + 1$ weighted deviation rows plus $n$ rows for the -shock standard deviations. +means and covariances from weighted statistics. Its QR decomposition operates on a +matrix of shape $(3n + 1) \times n$: the $2n + 1$ weighted deviation rows plus $n$ rows +for the shock standard deviations. The linear predict skips sigma-point generation entirely. Because the transition is -linear, the predicted mean is just a matrix--vector product, and the predicted covariance -follows from the standard linear Gaussian formula. Its QR decomposition operates on a -$(2n) \times n$ matrix: $n$ rows from the propagated Cholesky factor and $n$ rows for the -shocks. The reduction from $3n + 1$ to $2n$ rows speeds up the QR step and removes all -sigma-point overhead. +linear, the predicted mean is just a matrix--vector product, and the predicted +covariance follows from the standard linear Gaussian formula. Its QR decomposition +operates on a $(2n) \times n$ matrix: $n$ rows from the propagated Cholesky factor and +$n$ rows for the shocks. The reduction from $3n + 1$ to $2n$ rows speeds up the QR step +and removes all sigma-point overhead. The memory savings can be more important than the speed gains. The unscented path materialises $2n + 1$ sigma points for every observation and mixture component, and @@ -39,8 +39,8 @@ model and running out of memory. The linear predict assembles a transition matrix $F$ of shape $(n_\text{latent}, n_\text{all})$ and a constant vector $c$ of length $n_\text{latent}$ -from the `trans_coeffs` dictionary. Here $n_\text{all}$ includes both latent and observed -factors. +from the `trans_coeffs` dictionary. Here $n_\text{all}$ includes both latent and +observed factors. For each latent factor $i$: @@ -71,10 +71,10 @@ factors and constants, and $s^{\text{out}}$ and $c^{\text{out}}$ the output-peri counterparts. The steps are: 1. **Anchor** the input states: $x^a = x \odot s^{\text{in}} + c^{\text{in}}$. -2. **Concatenate** observed factors to form the full state vector +1. **Concatenate** observed factors to form the full state vector $\tilde{x} = [x^a, x^{\text{obs}}]$. -3. **Apply the linear transition**: $y^a = \tilde{x}\, F^\top + c$. -4. **Un-anchor** to get the predicted states: +1. **Apply the linear transition**: $y^a = \tilde{x}\, F^\top + c$. +1. **Un-anchor** to get the predicted states: $\hat{x} = (y^a - c^{\text{out}}) \oslash s^{\text{out}}$. ## Covariance prediction (square-root form) @@ -123,10 +123,10 @@ $F$ rather than the full matrix. ## Practical impact Benchmarks on a 4-factor linear model (`health-cognition`, -`no_feedback_to_investments_linear`, 8 GiB GPU) show a modest ~6 % speed-up on GPU -(8.4 vs 8.9 s per optimizer iteration) and negligible difference on CPU. The speed gain -is small because with only 4 latent factors the unscented transform generates just 9 -sigma points — a trivially cheap operation on modern hardware. +`no_feedback_to_investments_linear`, 8 GiB GPU) show a modest ~6 % speed-up on GPU (8.4 +vs 8.9 s per optimizer iteration) and negligible difference on CPU. The speed gain is +small because with only 4 latent factors the unscented transform generates just 9 sigma +points — a trivially cheap operation on modern hardware. The memory reduction is the more significant benefit. Under the same conditions the unscented path ran out of GPU memory when only ~5 GiB was free, while the linear path diff --git a/docs/explanations/names_and_concepts.md b/docs/explanations/names_and_concepts.md index f1b7b554..4aefd0ba 100644 --- a/docs/explanations/names_and_concepts.md +++ b/docs/explanations/names_and_concepts.md @@ -34,16 +34,17 @@ A **development stage** is a group of consecutive periods where the skill format technology (transition function parameters) remains constant. Stages are just equality constraints on parameters. -Example: With 5 periods, you can estimate at most 4 different transition functions. -The stagemap `[0, 0, 1, 1]` means: +Example: With 5 periods, you can estimate at most 4 different transition functions. The +stagemap `[0, 0, 1, 1]` means: + - Periods 0→1 and 1→2 share the same parameters (stage 0) - Periods 2→3 and 3→4 share the same parameters (stage 1) ## Augmented Periods -When models include endogenous factors (factors that depend on other factors in the -same period), skillmodels internally expands periods into "augmented periods" to handle -the sequential updating. Each regular period may contain multiple augmented periods. +When models include endogenous factors (factors that depend on other factors in the same +period), skillmodels internally expands periods into "augmented periods" to handle the +sequential updating. Each regular period may contain multiple augmented periods. ## Anchoring @@ -76,9 +77,9 @@ of factors are arbitrary). ## Estimation Options -Each estimator has its own options dataclass, passed at call time rather than -embedded in `ModelSpec`. The three classes share no fields — what counts as a -tuning knob differs between estimators. +Each estimator has its own options dataclass, passed at call time rather than embedded +in `ModelSpec`. The three classes share no fields — what counts as a tuning knob differs +between estimators. `CHSEstimationOptions` (from `skillmodels.chs`) controls the Kalman MLE: @@ -86,40 +87,37 @@ tuning knob differs between estimators. - **bounds_distance**: How much stricter to make bounds (zeroed if robust_bounds is false) - **sigma_points_scale**: Controls spread of sigma points in unscented Kalman filter -- **clipping_\***: Parameters for soft-clipping the log-likelihood to prevent +- **clipping\_\***: Parameters for soft-clipping the log-likelihood to prevent infinities -- **start_params_strategy**: How to seed the `params_template`. `"amn"` (default) - runs the full AMN three-stage estimator and uses its parameters as the start; - `"spearman"` uses moment-based start values; `"none"` leaves entries as NaN - for the caller to fill in. +- **start_params_strategy**: How to seed the `params_template`. `"amn"` (default) runs + the full AMN three-stage estimator and uses its parameters as the start; `"spearman"` + uses moment-based start values; `"none"` leaves entries as NaN for the caller to fill + in. `AFEstimationOptions` (from `skillmodels.af`) controls the sequential MLE: - **n_halton_points**, **n_halton_points_shock**: quadrature counts. - **optimizer_algorithm**: the optimagic algorithm name passed to - `optimagic.minimize(algorithm=...)` (default `"fides"`; use - `"scipy_lbfgsb"` for MC sweeps). -- **start_params_strategy**: `"amn"`, `"spearman"`, `"constant"`, or `"none"`. - Same meaning as in CHS. (The mixture-component count is not an estimator option; - it is the structural field `ModelSpec.n_mixtures`.) + `optimagic.minimize(algorithm=...)` (default `"fides"`; use `"scipy_lbfgsb"` for MC + sweeps). +- **start_params_strategy**: `"amn"`, `"spearman"`, `"constant"`, or `"none"`. Same + meaning as in CHS. (The mixture-component count is not an estimator option; it is the + structural field `ModelSpec.n_mixtures`.) -`AMNEstimationOptions` (from `skillmodels.amn`) controls the three-stage -pipeline: +`AMNEstimationOptions` (from `skillmodels.amn`) controls the three-stage pipeline: -- **em_max_iter**, **em_tol**, **em_n_init**, **em_reg_covar**: Stage-1 EM - numerical knobs. +- **em_max_iter**, **em_tol**, **em_n_init**, **em_reg_covar**: Stage-1 EM numerical + knobs. - **n_simulation_draws**: Stage-3 synthetic-panel size. -- **minimum_distance_weighting**: Stage-2 weighting. `"identity"` (default) is - the paper's unweighted identity-metric criterion over per-component means and - full covariance matrices, and is currently the only implemented option. - `"optimal"` is reserved for a future Avar-weighted criterion and raises - `NotImplementedError`. -- Investment-endogeneity correction is no longer an estimation-option flag. - Attach a `CorrectionSpec` to the endogenous investment `FactorSpec` - (`FactorSpec.correction`); its presence triggers the Stage-3 control-function - correction. See +- **minimum_distance_weighting**: Stage-2 weighting. `"identity"` (default) is the + paper's unweighted identity-metric criterion over per-component means and full + covariance matrices, and is currently the only implemented option. `"optimal"` is + reserved for a future Avar-weighted criterion and raises `NotImplementedError`. +- Investment-endogeneity correction is no longer an estimation-option flag. Attach a + `CorrectionSpec` to the endogenous investment `FactorSpec` (`FactorSpec.correction`); + its presence triggers the Stage-3 control-function correction. See [Endogeneity Corrections](../reference_guides/endogeneity_corrections.md). -The shared structural field — number of mixture components in the latent -distribution — lives directly on `ModelSpec.n_mixtures`, since it changes the -model itself rather than the optimizer. +The shared structural field — number of mixture components in the latent distribution — +lives directly on `ModelSpec.n_mixtures`, since it changes the model itself rather than +the optimizer. diff --git a/docs/explanations/notes_on_factor_scales.md b/docs/explanations/notes_on_factor_scales.md index 09e38e80..8bd88384 100644 --- a/docs/explanations/notes_on_factor_scales.md +++ b/docs/explanations/notes_on_factor_scales.md @@ -1,28 +1,30 @@ # Notes on Scales and Normalizations This section discusses factor scales and normalization, building on the -[critique by Wiswall and Agostinelli](https://tinyurl.com/y3wl43kz) of the original -CHS estimator. +[critique by Wiswall and Agostinelli](https://tinyurl.com/y3wl43kz) of the original CHS +estimator. Wiswall and Agostinelli define a class of transition functions with Known Location and Scale (KLS) that require fewer normalizations. Their critique potentially invalidates certain empirical estimates from CHS, but not the general estimation approach. To reduce the risk of renormalization issues, you can either: + 1. Use fewer normalizations with KLS transition functions, or -2. Use non-KLS transition functions with one normalization per period and factor +1. Use non-KLS transition functions with one normalization per period and factor skillmodels supports both schemes, but supporting a scheme is not the same as guaranteeing identification: the model checker performs syntactic checks on the -normalizations, not a transition-specific rank or scale-invariance analysis. -Whether a given scheme identifies the model still depends on the transition -functions and data; see the cautions below. +normalizations, not a transition-specific rank or scale-invariance analysis. Whether a +given scheme identifies the model still depends on the transition functions and data; +see the cautions below. + +As there is no natural scale for skills, neither approach is inherently better. However, +we prefer using flexible non-KLS transition functions with explicit normalizations +because: -As there is no natural scale for skills, neither approach is inherently better. -However, we prefer using flexible non-KLS transition functions with explicit -normalizations because: 1. They are more compatible with development stages spanning multiple periods -2. Suitable normalizations can give latent factors a more meaningful interpretation +1. Suitable normalizations can give latent factors a more meaningful interpretation ## Why KLS Functions Don't Keep Scales Constant @@ -30,6 +32,7 @@ After reading the Wiswall-Agostinelli critique, one might think that using KLS transition functions identifies some sort of "natural" scale. This is not the case. Consider a simple model of financial investments with two latent factors: + - **w**: wealth (stock variable) - **i**: investment (flow variable) @@ -51,22 +54,23 @@ This describes the exact same system in different scales. Any linear function co describe this system—just with different scale combinations. The CES function is KLS and contains all linear functions (without intercept) whose -parameters sum to 1. If we set both factor scales to dollars initially, the CES -function would choose: +parameters sum to 1. If we set both factor scales to dollars initially, the CES function +would choose: $$ w_{t+1} = \frac{1}{2.1}(1.1 w_t + i_t) \approx 0.524 w_t + 0.476 i_t $$ -This means wealth in period t+1 is measured in approximately 0.476 dollars—an -arbitrary choice made by the functional form, not something "natural." +This means wealth in period t+1 is measured in approximately 0.476 dollars—an arbitrary +choice made by the functional form, not something "natural." ## Why CES and log_CES Functions are Problematic -The KLS definition refers only to the scale of the output. But CES and log_CES -functions may also impose restrictions on input scales. +The KLS definition refers only to the scale of the output. But CES and log_CES functions +may also impose restrictions on input scales. Simulations suggest that with log_CES: + - You need initial location normalizations for all factors - You only need to normalize the scale of one factor initially @@ -76,28 +80,26 @@ than relying on automatic generation. ## Normalizations and Identification -The library distinguishes three separate things, and only the first two are -mechanical: +The library distinguishes three separate things, and only the first two are mechanical: -1. a **syntactic normalization** you supply through `Normalizations` or - `fixed_params`; -2. an estimator **precheck** that catches some missing initial scale/location - anchors — it is a precheck, **not** a proof of identification; -3. a **transition-family identification argument**, which the library does not - establish for arbitrary models. +1. a **syntactic normalization** you supply through `Normalizations` or `fixed_params`; +1. an estimator **precheck** that catches some missing initial scale/location anchors — + it is a precheck, **not** a proof of identification; +1. a **transition-family identification argument**, which the library does not establish + for arbitrary models. Because step 3 is on you, there is no single stage-level rule of thumb that is safe across transition functions. Use the template that matches your production function: -- **Direct trans-log** (`translog`, `translog_af`): anchor one nonzero loading and - one intercept/location for every independently scaled factor-period. -- **Restricted CES** (`log_ces_af` with $\psi_t = 1$): relative skill/investment - scales are identified through the production restrictions, so pinning *every* first - loading can impose testable restrictions — follow the CES templates in the - estimator-specific guides rather than normalizing mechanically. -- **Intentionally restricted (original-AMN) benchmark**: a deliberately - over-restricted spec used only as a comparison point; label it as such so the extra - restrictions are not mistaken for identification requirements. +- **Direct trans-log** (`translog`, `translog_af`): anchor one nonzero loading and one + intercept/location for every independently scaled factor-period. +- **Restricted CES** (`log_ces_af` with $\psi_t = 1$): relative skill/investment scales + are identified through the production restrictions, so pinning *every* first loading + can impose testable restrictions — follow the CES templates in the estimator-specific + guides rather than normalizing mechanically. +- **Intentionally restricted (original-AMN) benchmark**: a deliberately over-restricted + spec used only as a comparison point; label it as such so the extra restrictions are + not mistaken for identification requirements. For custom transitions you must establish identification yourself (or add a model-specific diagnostic); the automatic checker will not do it for you. diff --git a/docs/how_to_guides/how_to_compare_estimators.md b/docs/how_to_guides/how_to_compare_estimators.md index 89411c2e..7b5786e6 100644 --- a/docs/how_to_guides/how_to_compare_estimators.md +++ b/docs/how_to_guides/how_to_compare_estimators.md @@ -4,19 +4,17 @@ The getting-started tutorial shows the same `ModelSpec` estimated by all three estimators on CNLSY data. This guide picks up where the tutorial leaves off and quantifies the uncertainty around each estimator's point estimates: -1. **CHS**: analytic OPG / inverse-score standard errors from - `estimagic.estimate_ml`. -2. **AF**: propagated influence-function score bootstrap - (`compute_af_standard_errors`). -3. **AMN**: nonparametric cluster bootstrap (`compute_amn_standard_errors`). +1. **CHS**: analytic OPG / inverse-score standard errors from `estimagic.estimate_ml`. +1. **AF**: propagated influence-function score bootstrap (`compute_af_standard_errors`). +1. **AMN**: nonparametric cluster bootstrap (`compute_amn_standard_errors`). -The end of the guide overlays the three posterior factor trajectories on a -single panel so you can read off whether the estimators agree on the latent -factor path, not just on the parameter estimates. +The end of the guide overlays the three posterior factor trajectories on a single panel +so you can read off whether the estimators agree on the latent factor path, not just on +the parameter estimates. The guide assumes the three estimation results from the tutorial are in scope: -`chs_result`, `af_result`, and `amn_result`. The corresponding model and data -fixtures (`model`, `data`) are the same across all three. +`chs_result`, `af_result`, and `amn_result`. The corresponding model and data fixtures +(`model`, `data`) are the same across all three. The three estimators support different data features and model constructs; see [Estimator Prerequisites](../reference_guides/estimator_prerequisites.md) for the @@ -24,28 +22,27 @@ support matrix (measurement families, missing data, corrections, anchoring). ## Why each estimator gets a different inference -The three estimators can be applied to the same structural `ModelSpec`, but they -use different estimating criteria — CHS a joint Gaussian-component likelihood, AF -a sequential Halton-integrated likelihood, AMN a three-stage -EM / minimum-distance / simulate-and-regress fit — so their point estimates may -differ even before inference is considered. The sampling-distribution machinery -also differs: +The three estimators can be applied to the same structural `ModelSpec`, but they use +different estimating criteria — CHS a joint Gaussian-component likelihood, AF a +sequential Halton-integrated likelihood, AMN a three-stage EM / minimum-distance / +simulate-and-regress fit — so their point estimates may differ even before inference is +considered. The sampling-distribution machinery also differs: -| Estimator | Inference | Why this and not bootstrap (CHS) / not OPG (AF, AMN) | -| --------- | ---------------------------------------------------- | --------------------------------------------------------- | -| CHS | Analytic OPG / inverse-score (information equality) | Closed-form is valid under correct likelihood specification; bootstrap is just slower. | -| AF | Propagated influence-function score bootstrap | The closed-form variance ignores estimation error in period-$t-1$ nuisance params, biasing every period-$t \geq 1$ SE down. The influence-function score bootstrap propagates that earlier-period uncertainty, so cross-period covariances are non-zero. | -| AMN | Full re-estimation cluster bootstrap | The three-stage estimator has no clean sandwich form; each stage's residual variance compounds. | +| Estimator | Inference | Why this and not bootstrap (CHS) / not OPG (AF, AMN) | +| --------- | --------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| CHS | Analytic OPG / inverse-score (information equality) | Closed-form is valid under correct likelihood specification; bootstrap is just slower. | +| AF | Propagated influence-function score bootstrap | The closed-form variance ignores estimation error in period-$t-1$ nuisance params, biasing every period-$t \geq 1$ SE down. The influence-function score bootstrap propagates that earlier-period uncertainty, so cross-period covariances are non-zero. | +| AMN | Full re-estimation cluster bootstrap | The three-stage estimator has no clean sandwich form; each stage's residual variance compounds. | ## CHS: analytic standard errors -`estimate_chs` runs CHS through `estimagic.estimate_ml`, so its result carries -ML inference directly. By default `estimate_chs` sets `hessian=False` (the -numerical Hessian costs $O(\text{n\_params}^2)$ Kalman passes), so the reported -covariance is the **OPG / inverse-score** form — the inverse of the -outer-product-of-gradients information. This is valid under correct likelihood -specification (the information-equality assumption) but is **not** the -misspecification-robust sandwich, which would also need the Hessian. +`estimate_chs` runs CHS through `estimagic.estimate_ml`, so its result carries ML +inference directly. By default `estimate_chs` sets `hessian=False` (the numerical +Hessian costs $O(\text{n\_params}^2)$ Kalman passes), so the reported covariance is the +**OPG / inverse-score** form — the inverse of the outer-product-of-gradients +information. This is valid under correct likelihood specification (the +information-equality assumption) but is **not** the misspecification-robust sandwich, +which would also need the Hessian. ```python chs_result.likelihood_result.summary() # CHS via estimate_chs @@ -53,8 +50,8 @@ chs_result.likelihood_result.se() chs_result.likelihood_result.cov() ``` -To get the Hessian-based sandwich covariance instead, override the estimagic -keyword arguments via `CHSEstimationOptions.estimate_ml_options`: +To get the Hessian-based sandwich covariance instead, override the estimagic keyword +arguments via `CHSEstimationOptions.estimate_ml_options`: ```python from skillmodels import CHSEstimationOptions, estimate_chs @@ -68,24 +65,22 @@ chs_result = estimate_chs( If you drive the optimiser yourself via `get_maximization_inputs`, you can call `estimagic.estimate_ml` directly on `max_inputs["loglikeobs"]` and the -`max_inputs["constraints"]`, choosing `hessian=True`/`False` to pick the -sandwich or the OPG/inverse-score covariance. +`max_inputs["constraints"]`, choosing `hessian=True`/`False` to pick the sandwich or the +OPG/inverse-score covariance. ## AF: propagated influence-function score bootstrap -`compute_af_standard_errors` implements the Antweiler & Freyberger (2025) -§4.2 score bootstrap (after Armstrong, Bertanha & Hong 2014) in its -sequential-estimator influence-function form. It builds a single -per-observation influence matrix once at the optimum: each period block is a -one-step Newton update of that period's full-chain score that also carries -the earlier periods' influence via the cross-period (Hessian) blocks. For -each of `n_boot` replicates it draws *one shared* caseid index, resamples the -rows of the influence matrix with it, and shifts the estimate by the negated -resample mean. The shared index propagates earlier-period estimation -uncertainty, so the $t \geq 1$ standard errors are consistent and the -cross-period covariances are non-zero (unlike an own-block, independent-period -resample). No per-replicate re-estimation, so 10 000 replicates run in -seconds. +`compute_af_standard_errors` implements the Antweiler & Freyberger (2025) §4.2 score +bootstrap (after Armstrong, Bertanha & Hong 2014) in its sequential-estimator +influence-function form. It builds a single per-observation influence matrix once at the +optimum: each period block is a one-step Newton update of that period's full-chain score +that also carries the earlier periods' influence via the cross-period (Hessian) blocks. +For each of `n_boot` replicates it draws *one shared* caseid index, resamples the rows +of the influence matrix with it, and shifts the estimate by the negated resample mean. +The shared index propagates earlier-period estimation uncertainty, so the $t \geq 1$ +standard errors are consistent and the cross-period covariances are non-zero (unlike an +own-block, independent-period resample). No per-replicate re-estimation, so 10 000 +replicates run in seconds. ```python from skillmodels.af import compute_af_standard_errors @@ -102,22 +97,21 @@ af_inference.vcov # (n_params, n_params) DataFrame indexed by the params MultiI af_inference.replicate_params # (n_boot, n_params) ``` -The `replicate_params` DataFrame is the right object for plotting 95% -intervals: take the 2.5%/97.5% empirical quantiles per parameter rather than -$\hat{\theta} \pm 1.96 \cdot \mathrm{SE}$, since the one-step shifts can be -visibly skewed. +The `replicate_params` DataFrame is the right object for plotting 95% intervals: take +the 2.5%/97.5% empirical quantiles per parameter rather than +$\hat{\theta} \pm 1.96 \cdot \mathrm{SE}$, since the one-step shifts can be visibly +skewed. ## AMN: cluster bootstrap -AMN's three-stage pipeline (EM → minimum distance → simulate-and-regress) has -no analytic sandwich, so inference is a full cluster bootstrap: resample -caseids with replacement, re-run all three stages, repeat. Per-replicate cost -is dominated by the Stage 1 EM (~seconds for $n \approx 2000$, $K = 2$, -$\approx 40$ augmented measures). Each replicate draws a fresh per-replicate -seed (so the Stage-1 EM initialisation and the Stage-3 simulation vary across -replicates), and any replicate that fails to converge is excluded from the -bootstrap distribution and reported via a `RuntimeWarning`; its row in -`replicate_params` is `NaN`. +AMN's three-stage pipeline (EM → minimum distance → simulate-and-regress) has no +analytic sandwich, so inference is a full cluster bootstrap: resample caseids with +replacement, re-run all three stages, repeat. Per-replicate cost is dominated by the +Stage 1 EM (~seconds for $n \approx 2000$, $K = 2$, $\approx 40$ augmented measures). +Each replicate draws a fresh per-replicate seed (so the Stage-1 EM initialisation and +the Stage-3 simulation vary across replicates), and any replicate that fails to converge +is excluded from the bootstrap distribution and reported via a `RuntimeWarning`; its row +in `replicate_params` is `NaN`. ```python from skillmodels.amn import compute_amn_standard_errors @@ -133,8 +127,8 @@ amn_inference.standard_errors.head() amn_inference.replicate_params # (n_boot, n_params) -- includes failed replicates as NaN rows ``` -Bumping `n_boot` to 1000 is reasonable on a multi-core machine; the paper's -original AMN application uses 100. +Bumping `n_boot` to 1000 is reasonable on a multi-core machine; the paper's original AMN +application uses 100. ## Overlaying CES production-function CIs @@ -143,45 +137,53 @@ Side-by-side $\phi$ estimates with 95% CIs: ```python import pandas as pd + def _ci(replicate_params, param_loc, q=0.025): samples = replicate_params[param_loc].dropna() return samples.quantile(q), samples.quantile(1 - q) + rows = [] for period in (0, 1): phi_loc = ("transition", period, "skills", "phi") - rows.append({ - "period": period, - "estimator": "CHS", - "estimate": chs_result.params.loc[phi_loc, "value"], - "lower": chs_result.likelihood_result.summary().loc[phi_loc, "ci_lower"], - "upper": chs_result.likelihood_result.summary().loc[phi_loc, "ci_upper"], - }) - rows.append({ - "period": period, - "estimator": "AF", - "estimate": af_result.params.loc[phi_loc, "value"], - "lower": _ci(af_inference.replicate_params, phi_loc)[0], - "upper": _ci(af_inference.replicate_params, phi_loc)[1], - }) - rows.append({ - "period": period, - "estimator": "AMN", - "estimate": amn_result.params.loc[phi_loc, "value"], - "lower": _ci(amn_inference.replicate_params, phi_loc)[0], - "upper": _ci(amn_inference.replicate_params, phi_loc)[1], - }) + rows.append( + { + "period": period, + "estimator": "CHS", + "estimate": chs_result.params.loc[phi_loc, "value"], + "lower": chs_result.likelihood_result.summary().loc[phi_loc, "ci_lower"], + "upper": chs_result.likelihood_result.summary().loc[phi_loc, "ci_upper"], + } + ) + rows.append( + { + "period": period, + "estimator": "AF", + "estimate": af_result.params.loc[phi_loc, "value"], + "lower": _ci(af_inference.replicate_params, phi_loc)[0], + "upper": _ci(af_inference.replicate_params, phi_loc)[1], + } + ) + rows.append( + { + "period": period, + "estimator": "AMN", + "estimate": amn_result.params.loc[phi_loc, "value"], + "lower": _ci(amn_inference.replicate_params, phi_loc)[0], + "upper": _ci(amn_inference.replicate_params, phi_loc)[1], + } + ) phi_comparison = pd.DataFrame(rows) ``` ## Posterior factor trajectories -The three estimators produce different posterior beliefs about the latent -factor paths. `chs_states`, `af_states`, `amn_states` (built in the tutorial -via `get_individual_states`, `get_af_posterior_states`, -`get_amn_posterior_states`) all share a `period` column and one column per -factor, so a single melt + facet plot covers the comparison: +The three estimators produce different posterior beliefs about the latent factor paths. +`chs_states`, `af_states`, `amn_states` (built in the tutorial via +`get_individual_states`, `get_af_posterior_states`, `get_amn_posterior_states`) all +share a `period` column and one column per factor, so a single melt + facet plot covers +the comparison: ```python import plotly.express as px @@ -206,33 +208,30 @@ fig = px.line( fig.show() ``` -For a stronger visual comparison, plot the cross-individual variance band -($q_{0.1}$, $q_{0.5}$, $q_{0.9}$) per estimator side-by-side; agreement on the -median path with disagreement on the band is a useful diagnostic about how -the estimator treats the tail of the latent distribution. +For a stronger visual comparison, plot the cross-individual variance band ($q_{0.1}$, +$q_{0.5}$, $q_{0.9}$) per estimator side-by-side; agreement on the median path with +disagreement on the band is a useful diagnostic about how the estimator treats the tail +of the latent distribution. ## When the estimators disagree -If CHS, AF, and AMN disagree by more than the bootstrap CIs predict, the -candidate explanations are: - -- **Non-Gaussian latent factors.** CHS assumes Gaussian-mixture latents; AF and - AMN are more flexible about the mixture. Run `decompose_measurement_variance` - on each (the tutorial does this) and check whether the signal fractions - diverge — that's the leading indicator. -- **Misspecified transition function.** `log_ces` enforces a CES form via the - simplex constraint on the $\gamma$ weights; if the data prefers a linear - technology with a free constant, the CHS optimum can land in a different - basin than the AF/AMN sequential estimates that escape the constraint via - their integration weights. -- **Endogenous investment misalignment.** If `investment` is meant to be - endogenous (`is_endogenous=True`), CHS uses augmented periods internally - while AF treats it as a regular state per calendar period. The two answers - should still agree, but the augmented-period plumbing has historically been - the source of subtle bugs — start the diagnosis here if the disagreement is - concentrated around investment. +If CHS, AF, and AMN disagree by more than the bootstrap CIs predict, the candidate +explanations are: + +- **Non-Gaussian latent factors.** CHS assumes Gaussian-mixture latents; AF and AMN are + more flexible about the mixture. Run `decompose_measurement_variance` on each (the + tutorial does this) and check whether the signal fractions diverge — that's the + leading indicator. +- **Misspecified transition function.** `log_ces` enforces a CES form via the simplex + constraint on the $\gamma$ weights; if the data prefers a linear technology with a + free constant, the CHS optimum can land in a different basin than the AF/AMN + sequential estimates that escape the constraint via their integration weights. +- **Endogenous investment misalignment.** If `investment` is meant to be endogenous + (`is_endogenous=True`), CHS uses augmented periods internally while AF treats it as a + regular state per calendar period. The two answers should still agree, but the + augmented-period plumbing has historically been the source of subtle bugs — start the + diagnosis here if the disagreement is concentrated around investment. See [How to estimate AF](how_to_estimate_af.md) and -[How to estimate AMN](how_to_estimate_amn.md) for the estimator-specific tuning -that matters when the headline disagreement turns out to be numerical, not -substantive. +[How to estimate AMN](how_to_estimate_amn.md) for the estimator-specific tuning that +matters when the headline disagreement turns out to be numerical, not substantive. diff --git a/docs/how_to_guides/how_to_estimate_af.md b/docs/how_to_guides/how_to_estimate_af.md index 07708e76..818193b6 100644 --- a/docs/how_to_guides/how_to_estimate_af.md +++ b/docs/how_to_guides/how_to_estimate_af.md @@ -1,10 +1,9 @@ # Estimate a Model with AF (sequential Halton MLE) -The Antweiler & Freyberger (2025) estimator (`skillmodels.af.estimate_af`) fits -each period in sequence: period 0 jointly estimates the initial-period -measurement system and the latent mixture; each subsequent period takes the -estimated conditional state distribution and runs a period-specific MLE over a -joint Halton design. +The Antweiler & Freyberger (2025) estimator (`skillmodels.af.estimate_af`) fits each +period in sequence: period 0 jointly estimates the initial-period measurement system and +the latent mixture; each subsequent period takes the estimated conditional state +distribution and runs a period-specific MLE over a joint Halton design. ## Minimal example @@ -29,50 +28,45 @@ model = ModelSpec( data: pd.DataFrame # long-format, indexed by (caseid, period) af_options = AFEstimationOptions( - n_halton_points=200, # main quadrature - n_halton_points_shock=50, # production-shock integration + n_halton_points=200, # main quadrature + n_halton_points_shock=50, # production-shock integration ) result = estimate_af(model, data, af_options) -result.params # canonical skillmodels params DataFrame -result.period_results[0] # per-period AFPeriodResult +result.params # canonical skillmodels params DataFrame +result.period_results[0] # per-period AFPeriodResult ``` -For score-bootstrap standard errors, pass the same `data` and -`af_options` to `compute_af_standard_errors`: +For score-bootstrap standard errors, pass the same `data` and `af_options` to +`compute_af_standard_errors`: ```python from skillmodels.af import compute_af_standard_errors -inference = compute_af_standard_errors( - result, data, af_options, n_boot=10_000, seed=0 -) +inference = compute_af_standard_errors(result, data, af_options, n_boot=10_000, seed=0) inference.standard_errors ``` -`compute_af_standard_errors` implements the propagated influence-function -score bootstrap of Antweiler & Freyberger (2025) §4.2 (after Armstrong, -Bertanha & Hong 2014). It builds a single per-observation influence matrix -once at the optimum — each period block carries the earlier periods' -estimation uncertainty through the cross-period blocks of the full-chain -Hessian — then resamples its caseid rows with one shared index per -replicate. Because the same index is used across periods, the resulting -$t \geq 1$ standard errors are consistent and the cross-period covariances -are non-zero. No per-replicate re-estimation is involved, so 10 000 -replicates run in seconds. The result exposes `standard_errors`, `vcov`, -and `replicate_params`. +`compute_af_standard_errors` implements the propagated influence-function score +bootstrap of Antweiler & Freyberger (2025) §4.2 (after Armstrong, Bertanha & Hong 2014). +It builds a single per-observation influence matrix once at the optimum — each period +block carries the earlier periods' estimation uncertainty through the cross-period +blocks of the full-chain Hessian — then resamples its caseid rows with one shared index +per replicate. Because the same index is used across periods, the resulting $t \geq 1$ +standard errors are consistent and the cross-period covariances are non-zero. No +per-replicate re-estimation is involved, so 10 000 replicates run in seconds. The result +exposes `standard_errors`, `vcov`, and `replicate_params`. ## Optimizer Each period's MLE runs through `optimagic.minimize` with the algorithm in -`AFEstimationOptions.optimizer_algorithm` (default `"fides"`; pass -`"scipy_lbfgsb"` for Monte Carlo sweeps where a deterministic stopping -rule matters). The parameter vector crosses host↔device once per -iteration: +`AFEstimationOptions.optimizer_algorithm` (default `"fides"`; pass `"scipy_lbfgsb"` for +Monte Carlo sweeps where a deterministic stopping rule matters). The parameter vector +crosses host↔device once per iteration: 1. optimagic hands a pandas DataFrame to the user-supplied `fun` / `fun_and_jac`. -1. The wrapper extracts the `"value"` column, pushes it to device, runs the - jitted log-likelihood, and copies the scalar + gradient back to numpy. +1. The wrapper extracts the `"value"` column, pushes it to device, runs the jitted + log-likelihood, and copies the scalar + gradient back to numpy. Pass scipy_lbfgsb stopping options through `optimizer_options`: @@ -91,73 +85,67 @@ af_options = AFEstimationOptions( ) ``` -AF internally creates the fixed and probability constraints implied by the -processed model: `FixedConstraintWithValue` (from normalisations / `fixed_params`) -and `ProbabilityConstraint` (from the `log_ces` `gamma` simplex). The public -`constraints=` argument is narrower — it honours only `om.EqualityConstraint` -groups whose selector is built with -`skillmodels.common.constraints.select_by_loc` (used for within-step and -cross-period equality restrictions). Other optimagic constraint kinds passed there -are ignored; supply any further fixed values through `fixed_params` instead. +AF internally creates the fixed and probability constraints implied by the processed +model: `FixedConstraintWithValue` (from normalisations / `fixed_params`) and +`ProbabilityConstraint` (from the `log_ces` `gamma` simplex). The public `constraints=` +argument is narrower — it honours only `om.EqualityConstraint` groups whose selector is +built with `skillmodels.common.constraints.select_by_loc` (used for within-step and +cross-period equality restrictions). Other optimagic constraint kinds passed there are +ignored; supply any further fixed values through `fixed_params` instead. ## Start-values strategy -`AFEstimationOptions.start_params_strategy` controls how the per-period -parameter templates are seeded: +`AFEstimationOptions.start_params_strategy` controls how the per-period parameter +templates are seeded: -- `"amn"` (default) — run the full AMN three-stage estimator upfront and use - its parameter estimates as start values. Most accurate, slowest. -- `"spearman"` — moment-based seeds from Spearman cross-covariances and - Bartlett-style residual variances. Fast; good enough for most diagnostics. -- `"constant"` — legacy 0.5 / data-scaled defaults; useful for regression - testing and reproducing pre-fix results. -- `"none"` — accepted for cross-estimator symmetry; behaves identically to - `"constant"` (AF always needs concrete per-period starts). +- `"amn"` (default) — run the full AMN three-stage estimator upfront and use its + parameter estimates as start values. Most accurate, slowest. +- `"spearman"` — moment-based seeds from Spearman cross-covariances and Bartlett-style + residual variances. Fast; good enough for most diagnostics. +- `"constant"` — legacy 0.5 / data-scaled defaults; useful for regression testing and + reproducing pre-fix results. +- `"none"` — accepted for cross-estimator symmetry; behaves identically to `"constant"` + (AF always needs concrete per-period starts). -`compute_af_standard_errors` does not re-run the optimizer per replicate, so -the choice of `start_params_strategy` does not enter the inference path: -the score bootstrap reuses the point estimate and only resamples the -precomputed influence matrix. +`compute_af_standard_errors` does not re-run the optimizer per replicate, so the choice +of `start_params_strategy` does not enter the inference path: the score bootstrap reuses +the point estimate and only resamples the precomputed influence matrix. ## Production transition functions -For an AF production function, use the AF-specific transition functions -`translog_af` (eq. 6: linear terms + pairwise interactions, NO square terms) -or `log_ces_af` (eq. 7: CES over the production factors only). They enumerate -parameters over the production factors (skill + investment) so observed -factors such as income do not leak in as free production coefficients. +For an AF production function, use the AF-specific transition functions `translog_af` +(eq. 6: linear terms + pairwise interactions, NO square terms) or `log_ces_af` (eq. 7: +CES over the production factors only). They enumerate parameters over the production +factors (skill + investment) so observed factors such as income do not leak in as free +production coefficients. The general-library transitions (`linear`, `translog`, `robust_translog`, -`linear_and_squares`, `log_ces`, `log_ces_with_constant`, `log_ces_general`) -enumerate parameters over *all* factors, including observed ones. Using one of -these for a (non-endogenous) production factor while observed factors are -present makes income enter the production function with its own free -coefficients, which changes the AF estimand (income should affect skills only -through the investment equation). `validate_af_model` emits a `UserWarning` in -that case; either switch to `translog_af` / `log_ces_af`, or pin every -observed-factor transition coefficient to 0 via `fixed_params`. +`linear_and_squares`, `log_ces`, `log_ces_with_constant`, `log_ces_general`) enumerate +parameters over *all* factors, including observed ones. Using one of these for a +(non-endogenous) production factor while observed factors are present makes income enter +the production function with its own free coefficients, which changes the AF estimand +(income should affect skills only through the investment equation). `validate_af_model` +emits a `UserWarning` in that case; either switch to `translog_af` / `log_ces_af`, or +pin every observed-factor transition coefficient to 0 via `fixed_params`. ## Anchoring and endogenous factors -Anchoring is currently only supported by the CHS path -(`get_maximization_inputs`). The AF path estimates the latent-factor scale -implied by the measurement-system normalisations; anchoring outcomes can be -added back into the model spec for downstream visualisation but do not enter -the AF likelihood. - -Endogenous factors (investment in period $t$ measured by inv-measures in -period $t$) are supported. See `tests/test_af_estimate.py` for a worked -example with `is_endogenous=True`. - -The AF likelihood implements only the exogenous-investment case -($\kappa_t = 0$): production and investment shocks are integrated as -independent draws. The endogenous-investment control function is not part of -the AF estimator. If the model declares a `CorrectionSpec` (via -`FactorSpec.correction`), `validate_af_model` raises `NotImplementedError`; -strip it with `ModelSpec.without_correction()` to run AF, or use -`estimate_chs` to estimate the correction. (Supplying `kappa` / `kappa_t` -parameters directly via `start_params` / `fixed_params` likewise raises -`NotImplementedError`.) See -[Endogeneity Corrections](../reference_guides/endogeneity_corrections.md) for -the full control-function interface and -[How to estimate AMN](how_to_estimate_amn.md) for the AMN route. +Anchoring is currently only supported by the CHS path (`get_maximization_inputs`). The +AF path estimates the latent-factor scale implied by the measurement-system +normalisations; anchoring outcomes can be added back into the model spec for downstream +visualisation but do not enter the AF likelihood. + +Endogenous factors (investment in period $t$ measured by inv-measures in period $t$) are +supported. See `tests/test_af_estimate.py` for a worked example with +`is_endogenous=True`. + +The AF likelihood implements only the exogenous-investment case ($\kappa_t = 0$): +production and investment shocks are integrated as independent draws. The +endogenous-investment control function is not part of the AF estimator. If the model +declares a `CorrectionSpec` (via `FactorSpec.correction`), `validate_af_model` raises +`NotImplementedError`; strip it with `ModelSpec.without_correction()` to run AF, or use +`estimate_chs` to estimate the correction. (Supplying `kappa` / `kappa_t` parameters +directly via `start_params` / `fixed_params` likewise raises `NotImplementedError`.) See +[Endogeneity Corrections](../reference_guides/endogeneity_corrections.md) for the full +control-function interface and [How to estimate AMN](how_to_estimate_amn.md) for the AMN +route. diff --git a/docs/how_to_guides/how_to_estimate_amn.md b/docs/how_to_guides/how_to_estimate_amn.md index c84ca977..1148b298 100644 --- a/docs/how_to_guides/how_to_estimate_amn.md +++ b/docs/how_to_guides/how_to_estimate_amn.md @@ -1,27 +1,26 @@ # Estimate a Model with AMN (three-stage mixture-of-normals) -The Attanasio-Meghir-Nix 2020 estimator (`skillmodels.amn.estimate_amn`) runs -three stages: +The Attanasio-Meghir-Nix 2020 estimator (`skillmodels.amn.estimate_amn`) runs three +stages: 1. **Mixture EM.** Fit a Gaussian mixture - $F_{M, X} = \sum_k \tau_k \, \mathcal{N}(\Pi_k, \Psi_k)$ to the augmented - measure vector $[M_{1:T}, X]$ (controls / instruments stacked alongside - measurements as zero-error rows). -2. **Minimum distance.** Recover structural parameters - $(\Lambda, A, \Sigma, \mu, \Omega)$ from the reduced-form - $(\Pi_k, \Psi_k)$ subject to factor-measurement assignment, scale - normalisations, and the mean-zero mixture restriction. -3. **Simulate and regress.** Draw a large synthetic latent-factor panel - from the fitted mixture and estimate the production function by - regression on the synthetic data. - -AMN shines when the latent factor distribution is non-Gaussian. CHS supports a -finite mixture of Gaussian initial states via `ModelSpec.n_mixtures`, but filters -each component with a Gaussian (square-root Kalman) recursion; setting -`n_mixtures=1` is the deliberately restricted single-Gaussian benchmark. AF also -supports multiple mixture components but fits them jointly with the -period-specific optimizer. AMN cleanly separates the mixture (Stage-1 EM) from -the structural recovery, so it models the non-Gaussianity explicitly. + $F_{M, X} = \sum_k \tau_k \, \mathcal{N}(\Pi_k, \Psi_k)$ to the augmented measure + vector $[M_{1:T}, X]$ (controls / instruments stacked alongside measurements as + zero-error rows). +1. **Minimum distance.** Recover structural parameters + $(\Lambda, A, \Sigma, \mu, \Omega)$ from the reduced-form $(\Pi_k, \Psi_k)$ subject + to factor-measurement assignment, scale normalisations, and the mean-zero mixture + restriction. +1. **Simulate and regress.** Draw a large synthetic latent-factor panel from the fitted + mixture and estimate the production function by regression on the synthetic data. + +AMN shines when the latent factor distribution is non-Gaussian. CHS supports a finite +mixture of Gaussian initial states via `ModelSpec.n_mixtures`, but filters each +component with a Gaussian (square-root Kalman) recursion; setting `n_mixtures=1` is the +deliberately restricted single-Gaussian benchmark. AF also supports multiple mixture +components but fits them jointly with the period-specific optimizer. AMN cleanly +separates the mixture (Stage-1 EM) from the structural recovery, so it models the +non-Gaussianity explicitly. ## Minimal example @@ -53,21 +52,20 @@ amn_options = AMNEstimationOptions( ) result = estimate_amn(model, data, amn_options) -result.params # canonical skillmodels params DataFrame -result.stages.mixture # Stage 1: reduced-form Pi, Psi, tau -result.stages.structural # Stage 2: Lambda, A, Sigma, mu, Omega -result.stages.production # Stage 3: production-function regression -result.success # AND across stage convergence flags +result.params # canonical skillmodels params DataFrame +result.stages.mixture # Stage 1: reduced-form Pi, Psi, tau +result.stages.structural # Stage 2: Lambda, A, Sigma, mu, Omega +result.stages.production # Stage 3: production-function regression +result.success # AND across stage convergence flags ``` ## When AMN beats CHS: a synthetic 2-mixture DGP -The smallest example that lets AMN's non-Gaussian fit show its advantage is a -1-factor / 3-period model where the latent skill is drawn from a non-trivial -mixture-of-normals. CHS with a single Gaussian component (`n_mixtures=1`) -produces biased production-function estimates on this DGP; AMN's Stage 1 EM -recovers the -mixture and the structural step undoes the bias. +The smallest example that lets AMN's non-Gaussian fit show its advantage is a 1-factor / +3-period model where the latent skill is drawn from a non-trivial mixture-of-normals. +CHS with a single Gaussian component (`n_mixtures=1`) produces biased +production-function estimates on this DGP; AMN's Stage 1 EM recovers the mixture and the +structural step undoes the bias. ```python import numpy as np @@ -139,96 +137,90 @@ result = estimate_amn( result.params.loc[("transition", 0, "skill", "skill"), "value"] # Should be close to 0.7 (the true slope). -result.stages.mixture.weights # tau, should be near (0.6, 0.4) up to label switching -result.stages.mixture.means # Pi_k for the augmented measure vector +result.stages.mixture.weights # tau, should be near (0.6, 0.4) up to label switching +result.stages.mixture.means # Pi_k for the augmented measure vector ``` Compare against a CHS fit of the same model with `n_mixtures=1` (the deliberately -restricted single-Gaussian benchmark) and verify that the slope estimate from CHS -is biased downward — that's the signal AMN was designed to capture. +restricted single-Gaussian benchmark) and verify that the slope estimate from CHS is +biased downward — that's the signal AMN was designed to capture. ## Tuning knobs ### Number of mixture components -`ModelSpec.n_mixtures` (not an `AMNEstimationOptions` field) controls the -flexibility of the Stage-1 EM fit; AMN reads it as the number of mixture -components $K$. The paper fixes $K = 2$; in practice values from 2 to 4 are -reasonable. Higher $K$ adds free parameters to the reduced-form fit but does -not change the structural model — the minimum-distance step constrains them. +`ModelSpec.n_mixtures` (not an `AMNEstimationOptions` field) controls the flexibility of +the Stage-1 EM fit; AMN reads it as the number of mixture components $K$. The paper +fixes $K = 2$; in practice values from 2 to 4 are reasonable. Higher $K$ adds free +parameters to the reduced-form fit but does not change the structural model — the +minimum-distance step constrains them. ### Stage-1 EM stability Stage 1 uses `sklearn.mixture.GaussianMixture` under the hood. The defaults -(`em_n_init=5`, `em_reg_covar=1e-6`) reliably converge on well-identified -models; if the EM warns about degenerate covariances, bump `em_reg_covar` to -`1e-4` first. The fit is initialised from a Spearman-moment guess for the -loadings, then projected back to the augmented-measure space; that -data-driven start beats random init by a wide margin. +(`em_n_init=5`, `em_reg_covar=1e-6`) reliably converge on well-identified models; if the +EM warns about degenerate covariances, bump `em_reg_covar` to `1e-4` first. The fit is +initialised from a Spearman-moment guess for the loadings, then projected back to the +augmented-measure space; that data-driven start beats random init by a wide margin. ### Stage-1 missing data `mixture_em_method` selects how Stage 1 handles incomplete measurement rows: - `"complete_case"` (default) fits `sklearn.mixture.GaussianMixture` on - listwise-complete rows and raises `InsufficientCompleteCasesError` when fewer - than `n_mixtures` rows are complete. -- `"missing_data"` fits an EM that marginalises over each row's missing entries, - valid under an ignorable (MAR) missingness assumption even when no row is - complete. + listwise-complete rows and raises `InsufficientCompleteCasesError` when fewer than + `n_mixtures` rows are complete. +- `"missing_data"` fits an EM that marginalises over each row's missing entries, valid + under an ignorable (MAR) missingness assumption even when no row is complete. A column that is never observed in any sampled row has unidentified moments; the -missing-data EM raises unless `allow_never_observed_measurements=True` is set -(intended only for seeding-style uses where such a column is tolerated). -`mixture_em_max_rows` optionally subsamples the EM input for speed. +missing-data EM raises unless `allow_never_observed_measurements=True` is set (intended +only for seeding-style uses where such a column is tolerated). `mixture_em_max_rows` +optionally subsamples the EM input for speed. ### Stage-2 weighting -`minimum_distance_weighting="identity"` (the paper's default, and currently the -only implemented option) is fast and robust: it is an unweighted identity-metric -criterion over per-component means and the full covariance matrices. The -`"optimal"` value is reserved for a future Avar-weighted criterion and currently -raises `NotImplementedError`. +`minimum_distance_weighting="identity"` (the paper's default, and currently the only +implemented option) is fast and robust: it is an unweighted identity-metric criterion +over per-component means and the full covariance matrices. The `"optimal"` value is +reserved for a future Avar-weighted criterion and currently raises +`NotImplementedError`. ### Stage-3 simulation size -`n_simulation_draws` controls Monte-Carlo error in the production-function -regression. The paper notes "the larger the data we draw the lower the -simulation error" (p. 2522); 100 000 is overkill for $n \approx 2000$. Drop -to 50 000 for iterating, then bump back to 100 000 for the final fit. The -RNG is fully reproducible via `seed`. +`n_simulation_draws` controls Monte-Carlo error in the production-function regression. +The paper notes "the larger the data we draw the lower the simulation error" (p. 2522); +100 000 is overkill for $n \approx 2000$. Drop to 50 000 for iterating, then bump back +to 100 000 for the final fit. The RNG is fully reproducible via `seed`. ## Inference -Inference is a cluster bootstrap that re-runs all three stages on each -replicate. Each replicate draws a fresh seed, so the Stage-1 EM initialisation -and Stage-3 simulation vary across replicates; replicates that fail to converge -are excluded from the distribution and reported via a warning. Wall-clock is -dominated by Stage 1 EM ($\approx$ seconds for $n \approx 2000$), so 1000 -replicates run in $\approx$ 10-30 minutes on a single machine. +Inference is a cluster bootstrap that re-runs all three stages on each replicate. Each +replicate draws a fresh seed, so the Stage-1 EM initialisation and Stage-3 simulation +vary across replicates; replicates that fail to converge are excluded from the +distribution and reported via a warning. Wall-clock is dominated by Stage 1 EM +($\approx$ seconds for $n \approx 2000$), so 1000 replicates run in $\approx$ 10-30 +minutes on a single machine. ```python from skillmodels.amn import compute_amn_standard_errors -inference = compute_amn_standard_errors( - result, data, amn_options, n_boot=1000, seed=0 -) +inference = compute_amn_standard_errors(result, data, amn_options, n_boot=1000, seed=0) inference.standard_errors inference.replicate_params # (n_boot, n_params); failed replicates are NaN ``` -The paper itself uses 100 replicates (Tables 5-6); 1000 gives smoother CIs -without changing the qualitative picture. +The paper itself uses 100 replicates (Tables 5-6); 1000 gives smoother CIs without +changing the qualitative picture. ## Endogenous investment (control-function correction) The AMN (2020) eq. 7-8 / AF Sec. 3.5 control-function correction is configured -declaratively, not via an estimation option: attach a `CorrectionSpec` to the -endogenous investment `FactorSpec` (which must set `is_endogenous=True`). Its -mere presence triggers the correction in Stage 3 — there is no -`investment_endogeneity` flag. See -[Endogeneity Corrections](../reference_guides/endogeneity_corrections.md) for -the full interface; the same `CorrectionSpec` is also read by `estimate_chs`. +declaratively, not via an estimation option: attach a `CorrectionSpec` to the endogenous +investment `FactorSpec` (which must set `is_endogenous=True`). Its mere presence +triggers the correction in Stage 3 — there is no `investment_endogeneity` flag. See +[Endogeneity Corrections](../reference_guides/endogeneity_corrections.md) for the full +interface; the same `CorrectionSpec` is also read by `estimate_chs`. ```python from skillmodels import CorrectionSpec @@ -244,40 +236,39 @@ model = base_model.with_correction( result = estimate_amn(model, data, amn_options) ``` -Per period, a first-stage investment equation `ln I_t ~ theta_t (+ observed -instruments Y_t)` is OLS-fit on the simulated panel; its residual -`eta_{I,t} = ln I_t - E[ln I_t | theta_t, Y_t]` is added as an additive `cf` -covariate (coefficient `kappa_t`, period- and output-specific) to each target -factor's production regression. Under the correction: +Per period, a first-stage investment equation +`ln I_t ~ theta_t (+ observed instruments Y_t)` is OLS-fit on the simulated panel; its +residual `eta_{I,t} = ln I_t - E[ln I_t | theta_t, Y_t]` is added as an additive `cf` +covariate (coefficient `kappa_t`, period- and output-specific) to each target factor's +production regression. Under the correction: -- the instruments act as excluded regressors: they enter the first-stage - equation only, never a target's production function; +- the instruments act as excluded regressors: they enter the first-stage equation only, + never a target's production function; - at least one instrument is REQUIRED — `CorrectionSpec.__post_init__` raises - `ValueError` otherwise, because the residual would be collinear with the - production inputs and `kappa` would be unidentified; -- the first-stage coefficients and shock SD are returned under the - `investment_eq` / `investment_sds` categories on - `result.stages.production.investment_params`, and the production shock SD - (`shock_sds`) is the corrected SD(eps_C); -- AMN implements only the **linear** `cf` term. A higher-order `kappa_terms` - request (anything other than `("cf",)` per target, e.g. a `kappa_degree=2` - translog basis) raises `NotImplementedError` in `simulate_and_regress`; use - `estimate_chs` for the full polynomial basis. + `ValueError` otherwise, because the residual would be collinear with the production + inputs and `kappa` would be unidentified; +- the first-stage coefficients and shock SD are returned under the `investment_eq` / + `investment_sds` categories on `result.stages.production.investment_params`, and the + production shock SD (`shock_sds`) is the corrected SD(eps_C); +- AMN implements only the **linear** `cf` term. A higher-order `kappa_terms` request + (anything other than `("cf",)` per target, e.g. a `kappa_degree=2` translog basis) + raises `NotImplementedError` in `simulate_and_regress`; use `estimate_chs` for the + full polynomial basis. ## What AMN does not (yet) do -- **Anchoring** is not wired through the AMN stages. The model spec's - `AnchoringSpec` is accepted (so the spec stays compatible with CHS), but - the AMN result reports unanchored factor scales. -- **`start_params` and `constraints`.** `estimate_amn` does not honour either - and raises `NotImplementedError` if you pass them — the three-stage pipeline - has no single parameter vector to seed or constrain. -- **`fixed_params`** are honoured only for the categories each stage estimates, - and pinned inside that stage rather than post-hoc: Stage 2 honours `loadings`, - measurement intercepts, and measurement SDs; Stage 3 honours `transition`. - Passing any other category raises `NotImplementedError`. (The generic Stage-3 - NLS path supports pinning for most transitions, but `log_ces` / - `log_ces_with_constant` reject a non-empty fixed set.) - -See [How to compare estimators](how_to_compare_estimators.md) for an -overlay of CHS, AF, and AMN on the same data with confidence intervals. +- **Anchoring** is not wired through the AMN stages. The model spec's `AnchoringSpec` is + accepted (so the spec stays compatible with CHS), but the AMN result reports + unanchored factor scales. +- **`start_params` and `constraints`.** `estimate_amn` does not honour either and raises + `NotImplementedError` if you pass them — the three-stage pipeline has no single + parameter vector to seed or constrain. +- **`fixed_params`** are honoured only for the categories each stage estimates, and + pinned inside that stage rather than post-hoc: Stage 2 honours `loadings`, measurement + intercepts, and measurement SDs; Stage 3 honours `transition`. Passing any other + category raises `NotImplementedError`. (The generic Stage-3 NLS path supports pinning + for most transitions, but `log_ces` / `log_ces_with_constant` reject a non-empty fixed + set.) + +See [How to compare estimators](how_to_compare_estimators.md) for an overlay of CHS, AF, +and AMN on the same data with confidence intervals. diff --git a/docs/how_to_guides/model_specs.md b/docs/how_to_guides/model_specs.md index e3d302e9..ca78ca8d 100644 --- a/docs/how_to_guides/model_specs.md +++ b/docs/how_to_guides/model_specs.md @@ -42,10 +42,10 @@ model = ModelSpec( ) ``` -The `ModelSpec` is purely structural -- it describes the model, not how to -estimate it. Estimator-specific tuning (number of Halton draws, mixture -components in CHS Kalman, sigma-point scale, ...) lives on the relevant -options class and is passed at the call site: +The `ModelSpec` is purely structural -- it describes the model, not how to estimate it. +Estimator-specific tuning (number of Halton draws, mixture components in CHS Kalman, +sigma-point scale, ...) lives on the relevant options class and is passed at the call +site: ```python from skillmodels.chs import CHSEstimationOptions, get_maximization_inputs @@ -68,25 +68,24 @@ Each factor requires: Empty tuples indicate no measurements in that period. - **transition_function**: Name of a transition function (`linear`, `log_ces`, `constant`, `translog`, ...) or a custom function. See - [Transition Functions](../reference_guides/transition_functions.md) for the full - list. For an **AF production function**, prefer the production-factors-only - variants `translog_af` (AF eq. 6: linear + pairwise interactions, no squares) - and `log_ces_af` (AF eq. 7: CES over production factors only). The general - built-in transitions (`translog`, `log_ces`, ...) enumerate parameters over - **all** factors, including observed ones, so income and other observed factors - would receive free production coefficients — which changes the AF estimand. - `estimate_af` emits a `UserWarning` if you use a general built-in transition on - a production factor while observed factors are present. + [Transition Functions](../reference_guides/transition_functions.md) for the full list. + For an **AF production function**, prefer the production-factors-only variants + `translog_af` (AF eq. 6: linear + pairwise interactions, no squares) and `log_ces_af` + (AF eq. 7: CES over production factors only). The general built-in transitions + (`translog`, `log_ces`, ...) enumerate parameters over **all** factors, including + observed ones, so income and other observed factors would receive free production + coefficients — which changes the AF estimand. `estimate_af` emits a `UserWarning` if + you use a general built-in transition on a production factor while observed factors + are present. - **normalizations** (optional): Fixed values for loadings and intercepts to identify the model. The model checker validates these syntactically but does not prove transition-specific identification; see [Notes on factor scales](../explanations/notes_on_factor_scales.md). -- **is_endogenous** (optional): Whether this factor is endogenous (default: false). - See [Endogeneity Corrections](../reference_guides/endogeneity_corrections.md). -- **correction** (optional): A `CorrectionSpec | None` attached to an endogenous - investment factor, adding a control-function correction for investment - endogeneity. See +- **is_endogenous** (optional): Whether this factor is endogenous (default: false). See [Endogeneity Corrections](../reference_guides/endogeneity_corrections.md). +- **correction** (optional): A `CorrectionSpec | None` attached to an endogenous + investment factor, adding a control-function correction for investment endogeneity. + See [Endogeneity Corrections](../reference_guides/endogeneity_corrections.md). ## Anchoring @@ -95,10 +94,10 @@ Anchoring links latent factors to observable outcomes. Options: - **outcomes**: Dictionary mapping factor names to anchoring outcome variables - **free_controls**: Whether to estimate control coefficients in anchoring equations (default: false) -- **free_constant**: Whether to estimate a constant in anchoring equations - (default: false) -- **free_loadings**: Whether to estimate loadings in anchoring equations - (default: false) +- **free_constant**: Whether to estimate a constant in anchoring equations (default: + false) +- **free_loadings**: Whether to estimate loadings in anchoring equations (default: + false) - **ignore_constant_when_anchoring**: Skip constant when anchoring (default: false) ## Controls @@ -128,11 +127,10 @@ model = ModelSpec( ## Estimation Options -`n_mixtures` is a structural field on `ModelSpec` itself — the number of components -in the latent-factor mixture (default 1). The numerical knobs below are -**CHS-specific** and live on `CHSEstimationOptions` (`skillmodels.chs`), not on -`ModelSpec`; AF and AMN have their own option dataclasses (`AFEstimationOptions`, -`AMNEstimationOptions`). +`n_mixtures` is a structural field on `ModelSpec` itself — the number of components in +the latent-factor mixture (default 1). The numerical knobs below are **CHS-specific** +and live on `CHSEstimationOptions` (`skillmodels.chs`), not on `ModelSpec`; AF and AMN +have their own option dataclasses (`AFEstimationOptions`, `AMNEstimationOptions`). - **robust_bounds**: Make bounds stricter to avoid numerical issues (default: true) - **bounds_distance**: How much stricter to make bounds (default: 0.001) @@ -149,12 +147,14 @@ Define custom transition equations using the `@register_params` decorator: ```python from skillmodels.common.decorators import register_params + @register_params(params=["lincoeff"]) def my_linear(fac, params): return params["lincoeff"] * fac ``` Custom functions must: + - Accept `params` as a required argument (dictionary with registered parameters) - Accept factor values as floats or use `states` for a JAX array of all states - Return a float diff --git a/docs/index.md b/docs/index.md index 9c07d585..dd544e0a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,10 +1,10 @@ # skillmodels -Skillmodels is a Python toolbox for estimating nonlinear dynamic latent factor -models. It started as a Kalman-filter implementation of Cunha, Heckman & Schennach -([Econometrica 2010](http://onlinelibrary.wiley.com/doi/10.3982/ECTA6551/abstract)) -and has since grown to host three estimators side by side, all sharing the same -`ModelSpec` and the same parameter index. +Skillmodels is a Python toolbox for estimating nonlinear dynamic latent factor models. +It started as a Kalman-filter implementation of Cunha, Heckman & Schennach +([Econometrica 2010](http://onlinelibrary.wiley.com/doi/10.3982/ECTA6551/abstract)) and +has since grown to host three estimators side by side, all sharing the same `ModelSpec` +and the same parameter index. ## Overview @@ -12,44 +12,47 @@ Skillmodels was developed for skill-formation research but works for any dynamic nonlinear latent-factor model. Key features: - **Three estimators with one model spec**: + - `chs` — Kalman MLE (CHS 2010), the historical core. - `af` — sequential Halton-quadrature MLE (Antweiler & Freyberger 2025), period-by-period. - - `amn` — three-stage mixture-of-normals (Attanasio, Meghir & Nix 2020): - EM, minimum distance, simulated regression. - - The three estimators do **not** support the same data features and model - constructs (measurement families, missing data, corrections, anchoring). See - [Estimator Prerequisites](reference_guides/estimator_prerequisites.md) for the - full comparison before choosing one. -- **Strongly-typed, immutable model spec**: frozen dataclasses with - `MappingProxyType` containers throughout. + - `amn` — three-stage mixture-of-normals (Attanasio, Meghir & Nix 2020): EM, minimum + distance, simulated regression. + + The three estimators do **not** support the same data features and model constructs + (measurement families, missing data, corrections, anchoring). See + [Estimator Prerequisites](reference_guides/estimator_prerequisites.md) for the full + comparison before choosing one. + +- **Strongly-typed, immutable model spec**: frozen dataclasses with `MappingProxyType` + containers throughout. + - **JAX everywhere**: jitted likelihoods, autodiff gradients, optional GPU. ## Public API -The top-level `skillmodels` package re-exports the model-spec dataclasses that -every estimator consumes: +The top-level `skillmodels` package re-exports the model-spec dataclasses that every +estimator consumes: - `ModelSpec` - `FactorSpec` - `AnchoringSpec` - `Normalizations` -- `CorrectionSpec` — control-function correction for an endogenous investment - factor, read by both CHS and AMN (see +- `CorrectionSpec` — control-function correction for an endogenous investment factor, + read by both CHS and AMN (see [Endogeneity Corrections](reference_guides/endogeneity_corrections.md)). -The helper `generate_kappa_terms` (also top-level) builds the `cf`-interaction -monomial basis you pass as a `CorrectionSpec` target's `kappa_terms`. +The helper `generate_kappa_terms` (also top-level) builds the `cf`-interaction monomial +basis you pass as a `CorrectionSpec` target's `kappa_terms`. -Estimator-specific entry points live in their own subpackages so the scope of -each call is explicit at the import site: +Estimator-specific entry points live in their own subpackages so the scope of each call +is explicit at the import site: ```python from skillmodels.chs import ( CHSEstimationOptions, - estimate_chs, # one-call CHS estimator with ML inference - get_maximization_inputs, # likelihood + gradients + constraints for optimagic + estimate_chs, # one-call CHS estimator with ML inference + get_maximization_inputs, # likelihood + gradients + constraints for optimagic ) from skillmodels.af import ( AFEstimationOptions, @@ -63,13 +66,13 @@ from skillmodels.amn import ( ) ``` -`estimate_chs` is the turnkey CHS driver: it wraps `get_maximization_inputs` -and `estimagic.estimate_ml`, so the returned `CHSEstimationResult` carries ML -inference (via `result.likelihood_result`). `get_maximization_inputs` remains -the power-user escape hatch for callers who want to drive the optimiser -themselves. The estimator entry points (`estimate_chs`, `estimate_af`, -`estimate_amn`) and the `CorrectionSpec` / `generate_kappa_terms` helpers are -also importable directly from the top-level `skillmodels` package. +`estimate_chs` is the turnkey CHS driver: it wraps `get_maximization_inputs` and +`estimagic.estimate_ml`, so the returned `CHSEstimationResult` carries ML inference (via +`result.likelihood_result`). `get_maximization_inputs` remains the power-user escape +hatch for callers who want to drive the optimiser themselves. The estimator entry points +(`estimate_chs`, `estimate_af`, `estimate_amn`) and the `CorrectionSpec` / +`generate_kappa_terms` helpers are also importable directly from the top-level +`skillmodels` package. Estimator-agnostic helpers live under `skillmodels.common`: @@ -86,10 +89,9 @@ from skillmodels.common.diagnostic_plots import ( from skillmodels.common.state_ranges import create_state_ranges ``` -The estimator-agnostic diagnostic and variance-decomposition helpers take -pre-computed DataFrames (`residuals`, `contributions`, `filtered_states`); the -caller produces them via the estimator they ran. See the how-to guides for -worked examples. +The estimator-agnostic diagnostic and variance-decomposition helpers take pre-computed +DataFrames (`residuals`, `contributions`, `filtered_states`); the caller produces them +via the estimator they ran. See the how-to guides for worked examples. ## Implementation Notes @@ -97,21 +99,21 @@ The CHS estimator differs from the original [replication files](https://tinyurl.com/yyuq2sa4) in two ways: 1. Supports normalization schemes designed to address the - [critique](https://tinyurl.com/y3wl43kz) of Wiswall and Agostinelli. The - model checker performs only syntactic checks on the supplied normalizations; - it does not run a transition-specific rank/invariance analysis, so it does - not by itself guarantee identification of an arbitrary scheme. -2. Uses robust square-root implementations of the Kalman filters. + [critique](https://tinyurl.com/y3wl43kz) of Wiswall and Agostinelli. The model + checker performs only syntactic checks on the supplied normalizations; it does not + run a transition-specific rank/invariance analysis, so it does not by itself + guarantee identification of an arbitrary scheme. +1. Uses robust square-root implementations of the Kalman filters. -The AF and AMN estimators are independent rewrites of the algorithms in their -respective papers and share only the `ModelSpec` and parameter-index machinery -with CHS; they do not call the Kalman filter. +The AF and AMN estimators are independent rewrites of the algorithms in their respective +papers and share only the `ModelSpec` and parameter-index machinery with CHS; they do +not call the Kalman filter. ## Citation If you find skillmodels helpful for research, please cite it. See the -[GitHub repository](https://github.com/OpenSourceEconomics/skillmodels) for -citation information. +[GitHub repository](https://github.com/OpenSourceEconomics/skillmodels) for citation +information. ## Feedback diff --git a/docs/reference_guides/endogeneity_corrections.md b/docs/reference_guides/endogeneity_corrections.md index c5899bd0..c3564c63 100644 --- a/docs/reference_guides/endogeneity_corrections.md +++ b/docs/reference_guides/endogeneity_corrections.md @@ -1,22 +1,22 @@ # Endogeneity Corrections When investment decisions depend on the concurrent latent state, investment is -endogenous: its shock is correlated with the production shock of the factors it -feeds. The **control-function** approach corrects for this by adding the -unobserved component of investment — a first-stage residual `cf` — to the -production equations of the affected factors. +endogenous: its shock is correlated with the production shock of the factors it feeds. +The **control-function** approach corrects for this by adding the unobserved component +of investment — a first-stage residual `cf` — to the production equations of the +affected factors. -skillmodels exposes this as a single, declarative object: a `CorrectionSpec` -attached to the endogenous investment factor. The same specification is read by -both estimators that implement the correction (CHS and AMN), so the control -function is configured in exactly one place regardless of which estimator runs. +skillmodels exposes this as a single, declarative object: a `CorrectionSpec` attached to +the endogenous investment factor. The same specification is read by both estimators that +implement the correction (CHS and AMN), so the control function is configured in exactly +one place regardless of which estimator runs. ## The `CorrectionSpec` interface -`CorrectionSpec` lives in `skillmodels.common.model_spec` and is re-exported -from the top-level package (`from skillmodels import CorrectionSpec`). You -declare it via the `correction=` field of the endogenous investment `FactorSpec` -(the factor must still set `is_endogenous=True`): +`CorrectionSpec` lives in `skillmodels.common.model_spec` and is re-exported from the +top-level package (`from skillmodels import CorrectionSpec`). You declare it via the +`correction=` field of the endogenous investment `FactorSpec` (the factor must still set +`is_endogenous=True`): ```python from skillmodels import CorrectionSpec, FactorSpec, ModelSpec, Normalizations @@ -50,51 +50,48 @@ model = ModelSpec( ) ``` -This is the model used in `tests/test_amn_simulate_and_regress.py` -(`_cf_model`); see also `tests/test_cf_recovery.py` for the end-to-end CHS -recovery test. +This is the model used in `tests/test_amn_simulate_and_regress.py` (`_cf_model`); see +also `tests/test_cf_recovery.py` for the end-to-end CHS recovery test. ### Fields -`CorrectionSpec` has the following fields (see -`src/skillmodels/common/model_spec.py`): - -- `instruments: tuple[str, ...]` — **required**, at least one. Excluded observed - factors that enter the first-stage investment equation **only** (never a - production equation), and so identify the correction coefficient `kappa`. With - no excluded instrument the residual would be collinear with the production - inputs and `kappa` would be unidentified (the `__post_init__` raises - `ValueError`). Instruments must be observed factors on the `ModelSpec`; - `ModelSpec.with_correction` registers them for you (see below). -- `state_predictors: tuple[str, ...] = ()` — state factors entering the - first-stage equation. Empty means **all** state factors. -- `targets: tuple[str, ...] = ()` — state factors whose production equation - receives the additive `kappa * cf` term. Empty means **all** state factors. -- `kappa_degree: int | None = None` — degree of the `cf`-interaction polynomial - applied to every target. `1` is a linear `cf` term; `2` is the translog basis. - `None` resolves to degree `1`. Mutually exclusive with `kappa_terms`. -- `kappa_terms: Mapping[str, tuple[str, ...]] | None = None` — per-target - override of the `cf` regressor names, e.g. - `{"skills": ("cf", "cf ** 2", "cf * skills")}`. Mutually exclusive with - `kappa_degree`. A target omitted from the mapping defaults to `("cf",)`. +`CorrectionSpec` has the following fields (see `src/skillmodels/common/model_spec.py`): + +- `instruments: tuple[str, ...]` — **required**, at least one. Excluded observed factors + that enter the first-stage investment equation **only** (never a production equation), + and so identify the correction coefficient `kappa`. With no excluded instrument the + residual would be collinear with the production inputs and `kappa` would be + unidentified (the `__post_init__` raises `ValueError`). Instruments must be observed + factors on the `ModelSpec`; `ModelSpec.with_correction` registers them for you (see + below). +- `state_predictors: tuple[str, ...] = ()` — state factors entering the first-stage + equation. Empty means **all** state factors. +- `targets: tuple[str, ...] = ()` — state factors whose production equation receives the + additive `kappa * cf` term. Empty means **all** state factors. +- `kappa_degree: int | None = None` — degree of the `cf`-interaction polynomial applied + to every target. `1` is a linear `cf` term; `2` is the translog basis. `None` resolves + to degree `1`. Mutually exclusive with `kappa_terms`. +- `kappa_terms: Mapping[str, tuple[str, ...]] | None = None` — per-target override of + the `cf` regressor names, e.g. `{"skills": ("cf", "cf ** 2", "cf * skills")}`. + Mutually exclusive with `kappa_degree`. A target omitted from the mapping defaults to + `("cf",)`. `kappa_degree` and `kappa_terms` are mutually exclusive; supplying both raises `ValueError`. ### How the `cf` regressors are resolved -Internally (in `process_model._resolve_control_function`) each target's -regressor list is resolved as follows: +Internally (in `process_model._resolve_control_function`) each target's regressor list +is resolved as follows: - if `kappa_terms` is set, target `t` uses `kappa_terms.get(t, ("cf",))`; -- otherwise the degree (defaulting to `1`) is expanded over the state factors - via `generate_kappa_terms`, and every target shares that basis. +- otherwise the degree (defaulting to `1`) is expanded over the state factors via + `generate_kappa_terms`, and every target shares that basis. ## Building the basis with `generate_kappa_terms` -`generate_kappa_terms` (top-level: `from skillmodels import -generate_kappa_terms`) builds the `cf`-interaction monomial basis you can pass -as a target's `kappa_terms`: +`generate_kappa_terms` (top-level: `from skillmodels import generate_kappa_terms`) +builds the `cf`-interaction monomial basis you can pass as a target's `kappa_terms`: ```python from skillmodels import generate_kappa_terms @@ -107,20 +104,20 @@ generate_kappa_terms(("skills", "health"), max_degree=2) ``` Its signature is -`generate_kappa_terms(factors, max_degree, max_cf_power=None) -> tuple[str, ...]`: -every monomial `cf ** a * prod_i factor_i ** b_i` with `a >= 1` and total degree -`a + sum_i b_i <= max_degree` (optionally capping the `cf` power at -`max_cf_power`). Pass the result as a target's `kappa_terms` and pin any unwanted -coefficients to zero with an optimagic constraint. +`generate_kappa_terms(factors, max_degree, max_cf_power=None) -> tuple[str, ...]`: every +monomial `cf ** a * prod_i factor_i ** b_i` with `a >= 1` and total degree +`a + sum_i b_i <= max_degree` (optionally capping the `cf` power at `max_cf_power`). +Pass the result as a target's `kappa_terms` and pin any unwanted coefficients to zero +with an optimagic constraint. ## Builder methods on `ModelSpec` -Two fluent builders make it easy to add or remove the correction without -rewriting the factor dict: +Two fluent builders make it easy to add or remove the correction without rewriting the +factor dict: - `ModelSpec.with_correction(factor_name, correction)` — attach `correction` to - `factor_name` **and** auto-register its instruments as observed factors - (deduped against existing ones), so instruments are declared exactly once: + `factor_name` **and** auto-register its instruments as observed factors (deduped + against existing ones), so instruments are declared exactly once: ```python model = base_model.with_correction( @@ -134,92 +131,88 @@ rewriting the factor dict: # "income" is now in model.observed_factors automatically. ``` -- `ModelSpec.without_correction()` — return a copy with every - `FactorSpec.correction` stripped. Useful for running an estimator that does not - implement the correction (AF) on a spec authored for CHS. +- `ModelSpec.without_correction()` — return a copy with every `FactorSpec.correction` + stripped. Useful for running an estimator that does not implement the correction (AF) + on a spec authored for CHS. -You can equivalently set `correction=` directly on the `FactorSpec` (as in the -first example), but then you must list the instruments in `observed_factors` -yourself. +You can equivalently set `correction=` directly on the `FactorSpec` (as in the first +example), but then you must list the instruments in `observed_factors` yourself. ## Estimator support The correction is read from the model spec by all three estimators, but only two implement it: -| Estimator | Control-function support | -| ---------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **CHS** (`estimate_chs` / `get_maximization_inputs`) | Full polynomial `kappa` basis. Any `kappa_degree` / `kappa_terms` is honoured; the `cf` nodes are grafted into the per-period transition DAG. | -| **AMN** (`estimate_amn`) | **Linear `cf` term only.** A higher-order `kappa_terms` request (anything other than `("cf",)` per target) raises `NotImplementedError` in Stage 3. | +| Estimator | Control-function support | +| ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **CHS** (`estimate_chs` / `get_maximization_inputs`) | Full polynomial `kappa` basis. Any `kappa_degree` / `kappa_terms` is honoured; the `cf` nodes are grafted into the per-period transition DAG. | +| **AMN** (`estimate_amn`) | **Linear `cf` term only.** A higher-order `kappa_terms` request (anything other than `("cf",)` per target) raises `NotImplementedError` in Stage 3. | | **AF** (`estimate_af`) | **Not implemented.** If any `FactorSpec.correction` is set, `validate_af_model` raises `NotImplementedError`, directing you to `estimate_chs` or `ModelSpec.without_correction()`. | -The AMN gate lives in `src/skillmodels/amn/simulate_and_regress.py`: if any -target's `kappa_terms` is not exactly `("cf",)`, it raises `NotImplementedError` -("AMN implements only a linear control function (kappa \* cf) ... use -estimate_chs for the full polynomial basis"). +The AMN gate lives in `src/skillmodels/amn/simulate_and_regress.py`: if any target's +`kappa_terms` is not exactly `("cf",)`, it raises `NotImplementedError` ("AMN implements +only a linear control function (kappa * cf) ... use estimate_chs for the full polynomial +basis"). -The AF gate lives in `src/skillmodels/af/validate.py`: AF covers only the -`kappa = 0` (exogenous-investment) special case, so any declared correction -raises `NotImplementedError`. Strip it with `ModelSpec.without_correction()` to -run AF. +The AF gate lives in `src/skillmodels/af/validate.py`: AF covers only the `kappa = 0` +(exogenous-investment) special case, so any declared correction raises +`NotImplementedError`. Strip it with `ModelSpec.without_correction()` to run AF. ## How it works internally -The CHS estimator does not require you to write a custom transition function for -the correction. `process_model` injects three kinds of synthetic node into the -per-period transition DAG (see `src/skillmodels/common/control_function.py`): +The CHS estimator does not require you to write a custom transition function for the +correction. `process_model` injects three kinds of synthetic node into the per-period +transition DAG (see `src/skillmodels/common/control_function.py`): -1. a deterministic, contemporaneous first-stage prediction - `E[ln I_t | theta_t, Y_t]` for the endogenous investment factor, fitted over - the `state_predictors` and `instruments`; -2. the residual `cf_t = ln I_t - E[ln I_t | theta_t, Y_t]`; and -3. for each target factor, the additive `sum_k kappa_k * cf_term_k` grafted onto - the factor's base transition output. +1. a deterministic, contemporaneous first-stage prediction `E[ln I_t | theta_t, Y_t]` + for the endogenous investment factor, fitted over the `state_predictors` and + `instruments`; +1. the residual `cf_t = ln I_t - E[ln I_t | theta_t, Y_t]`; and +1. for each target factor, the additive `sum_k kappa_k * cf_term_k` grafted onto the + factor's base transition output. -The first-stage coefficients appear in the params DataFrame under the -`investment_eq` category and the correction coefficients under the `kappa` -category. The corrected production-shock SD is recovered in `shock_sds`. +The first-stage coefficients appear in the params DataFrame under the `investment_eq` +category and the correction coefficients under the `kappa` category. The corrected +production-shock SD is recovered in `shock_sds`. Models with endogenous factors split each calendar period into augmented periods internally (`aug_period`); this is what lets the investment residual be measured -conditional on the current state before it enters the next factor's production -equation. Augmented periods are strictly internal — every public function -accepts and returns the user-facing `period`. +conditional on the current state before it enters the next factor's production equation. +Augmented periods are strictly internal — every public function accepts and returns the +user-facing `period`. ## Background: CHS Methods -Cunha, Heckman, and Schennach (2010) propose two alternative endogeneity -correction methods that rely on stronger assumptions about factor scales: +Cunha, Heckman, and Schennach (2010) propose two alternative endogeneity correction +methods that rely on stronger assumptions about factor scales: ### Time-Invariant Heterogeneity (Section 4.2.4) -Adds a time-invariant individual fixed effect. Requires constant factor scales -across all periods (highly unlikely with KLS transition functions), -age-invariant normalization measurements, and three adult outcomes. +Adds a time-invariant individual fixed effect. Requires constant factor scales across +all periods (highly unlikely with KLS transition functions), age-invariant normalization +measurements, and three adult outcomes. ### Time-Varying Heterogeneity (Section 4.2.5) Uses heterogeneity following an AR(1) process. Requires constant factor scales, a -time-invariant investment equation, and exclusion restrictions (e.g., income -affects investment but not skill transitions). +time-invariant investment equation, and exclusion restrictions (e.g., income affects +investment but not skill transitions). These methods are not implemented in skillmodels. If your dataset meets their -requirements, consider the original -[CHS Fortran code](https://tinyurl.com/yyuq2sa4). +requirements, consider the original [CHS Fortran code](https://tinyurl.com/yyuq2sa4). ## Legacy / migration -Earlier versions of skillmodels exposed the control function through a much more -manual interface. If you are migrating an old model spec, note: +Earlier versions of skillmodels exposed the control function through a much more manual +interface. If you are migrating an old model spec, note: -- There is **no separate correction factor.** A dedicated `is_correction=True` - factor paired with the endogenous factor is gone; `is_correction` is no longer - a `FactorSpec` field. Replace the pair with a single endogenous investment - factor carrying a `CorrectionSpec`. +- There is **no separate correction factor.** A dedicated `is_correction=True` factor + paired with the endogenous factor is gone; `is_correction` is no longer a `FactorSpec` + field. Replace the pair with a single endogenous investment factor carrying a + `CorrectionSpec`. - You no longer write the control function by hand. The old pattern — a custom transition decorated with `@register_params` that computed `cf = investment - investment_pred` inside the function body — is obsolete. The - library now forms `cf` and injects `kappa * cf` for you from the - `CorrectionSpec`. -- Period augmentation is unchanged but is now an internal detail (see "How it - works internally" above); you do not configure it directly. + library now forms `cf` and injects `kappa * cf` for you from the `CorrectionSpec`. +- Period augmentation is unchanged but is now an internal detail (see "How it works + internally" above); you do not configure it directly. diff --git a/docs/reference_guides/estimator_prerequisites.md b/docs/reference_guides/estimator_prerequisites.md index a6e50ec3..7a91d36f 100644 --- a/docs/reference_guides/estimator_prerequisites.md +++ b/docs/reference_guides/estimator_prerequisites.md @@ -13,20 +13,20 @@ three estimators. ## Prerequisites matrix -| Dimension | CHS | AF | AMN | -| --------- | --- | -- | --- | -| Estimator | Joint maximum likelihood with square-root Gaussian-component filtering. | Sequential period-by-period likelihood with Halton integration. | Three stages: Gaussian-mixture EM → minimum distance → simulate-and-regress. | -| Latent distribution | Finite mixture of Gaussian initial states via `ModelSpec.n_mixtures`; Gaussian component filtering thereafter. | Initial finite mixture plus sequentially carried distributions; Halton nodes approximate the integrals. | Stage-1 Gaussian mixture over the augmented measurement vector; `n_mixtures` sets the component count. | -| Measurement families | **Gaussian only** (standard Kalman update). Probit/Tobit measurements are not consumed by the CHS path. | **Initial period only**: probit/Tobit measurement families are honoured at the period-0 measurement system; transition periods ($t \geq 1$) fall back to an all-Gaussian measurement kernel. | **Gaussian only**: `estimate_amn` raises `NotImplementedError` if `ModelSpec.measurement_models` declares any probit/Tobit measurement. | -| Missing data | Gaussian measurement updates skip individually missing measurements. | Measurement masks skip missing rows in the per-step likelihood contributions. | `mixture_em_method="complete_case"` (default) or `"missing_data"` (marginalises over missing entries under MAR); never-observed columns require `allow_never_observed_measurements=True`. | -| Endogenous investment | Reconstructed endogenous factors plus a full `CorrectionSpec` control-function basis. | Reconstructed endogenous investment with independent shocks (source/destination calendar adapter); rejects `CorrectionSpec` / nonzero `kappa`. | Endogenous investment in Stage 3; correction support is a linearised control-function term, narrower than CHS. | -| Corrections | Full `CorrectionSpec` / `kappa` polynomial basis in the processed transition DAG. | Not implemented — the validator raises if a `CorrectionSpec` is attached. | Linear `cf` term only; a higher-order `kappa_terms` request raises. | -| Custom transitions | Built-ins and `@register_params` callables. | Built-ins and `@register_params` callables. | Built-ins and `@register_params` callables via the Stage-3 generic NLS path, but `log_ces` / `log_ces_with_constant` reject fixed parameters there. | -| `fixed_params` | Honoured through the shared parameter index. | Honoured (with public `constraints=` limited to `select_by_loc` equality groups). | Honoured only for the categories each stage estimates (Stage-2 loadings/intercepts/SDs, Stage-3 `transition`); other categories raise. `start_params` and `constraints` raise. | -| Normalization | Shared `Normalizations` / `fixed_params` / equality constraints. The checker is a precheck, not an identification proof. | Same public `ModelSpec`, with the AF-specific source/destination calendar and `af_state_role` metadata. | Stage-2 imposes its own structural moment restrictions and a mean-zero mixture convention. | -| Anchoring | Supported through `get_maximization_inputs` / the CHS path. | Not part of the AF likelihood; downstream visualization only. | Not wired through the AMN stages (the result reports unanchored scales). | -| Cost / scaling | Potentially expensive joint ML; the JAX square-root filter helps numerical stability. | Sequential but quadrature-heavy; cost grows with node count and state dimension. | Fast when the stages are well behaved; the cluster bootstrap is expensive because it re-estimates every stage. | -| Use it for | Likelihood benchmark, anchoring, correction-heavy models, Gaussian measurement systems. | Sequential AF-style models, initial-period limited measurements, period-by-period diagnostics. | Mixture-heavy Gaussian-measurement models, fast start values for CHS, Stage-1/2 structural diagnostics. | +| Dimension | CHS | AF | AMN | +| --------------------- | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| Estimator | Joint maximum likelihood with square-root Gaussian-component filtering. | Sequential period-by-period likelihood with Halton integration. | Three stages: Gaussian-mixture EM → minimum distance → simulate-and-regress. | +| Latent distribution | Finite mixture of Gaussian initial states via `ModelSpec.n_mixtures`; Gaussian component filtering thereafter. | Initial finite mixture plus sequentially carried distributions; Halton nodes approximate the integrals. | Stage-1 Gaussian mixture over the augmented measurement vector; `n_mixtures` sets the component count. | +| Measurement families | **Gaussian only** (standard Kalman update). Probit/Tobit measurements are not consumed by the CHS path. | **Initial period only**: probit/Tobit measurement families are honoured at the period-0 measurement system; transition periods ($t \geq 1$) fall back to an all-Gaussian measurement kernel. | **Gaussian only**: `estimate_amn` raises `NotImplementedError` if `ModelSpec.measurement_models` declares any probit/Tobit measurement. | +| Missing data | Gaussian measurement updates skip individually missing measurements. | Measurement masks skip missing rows in the per-step likelihood contributions. | `mixture_em_method="complete_case"` (default) or `"missing_data"` (marginalises over missing entries under MAR); never-observed columns require `allow_never_observed_measurements=True`. | +| Endogenous investment | Reconstructed endogenous factors plus a full `CorrectionSpec` control-function basis. | Reconstructed endogenous investment with independent shocks (source/destination calendar adapter); rejects `CorrectionSpec` / nonzero `kappa`. | Endogenous investment in Stage 3; correction support is a linearised control-function term, narrower than CHS. | +| Corrections | Full `CorrectionSpec` / `kappa` polynomial basis in the processed transition DAG. | Not implemented — the validator raises if a `CorrectionSpec` is attached. | Linear `cf` term only; a higher-order `kappa_terms` request raises. | +| Custom transitions | Built-ins and `@register_params` callables. | Built-ins and `@register_params` callables. | Built-ins and `@register_params` callables via the Stage-3 generic NLS path, but `log_ces` / `log_ces_with_constant` reject fixed parameters there. | +| `fixed_params` | Honoured through the shared parameter index. | Honoured (with public `constraints=` limited to `select_by_loc` equality groups). | Honoured only for the categories each stage estimates (Stage-2 loadings/intercepts/SDs, Stage-3 `transition`); other categories raise. `start_params` and `constraints` raise. | +| Normalization | Shared `Normalizations` / `fixed_params` / equality constraints. The checker is a precheck, not an identification proof. | Same public `ModelSpec`, with the AF-specific source/destination calendar and `af_state_role` metadata. | Stage-2 imposes its own structural moment restrictions and a mean-zero mixture convention. | +| Anchoring | Supported through `get_maximization_inputs` / the CHS path. | Not part of the AF likelihood; downstream visualization only. | Not wired through the AMN stages (the result reports unanchored scales). | +| Cost / scaling | Potentially expensive joint ML; the JAX square-root filter helps numerical stability. | Sequential but quadrature-heavy; cost grows with node count and state dimension. | Fast when the stages are well behaved; the cluster bootstrap is expensive because it re-estimates every stage. | +| Use it for | Likelihood benchmark, anchoring, correction-heavy models, Gaussian measurement systems. | Sequential AF-style models, initial-period limited measurements, period-by-period diagnostics. | Mixture-heavy Gaussian-measurement models, fast start values for CHS, Stage-1/2 structural diagnostics. | ## Reading the matrix diff --git a/docs/reference_guides/transition_functions.md b/docs/reference_guides/transition_functions.md index 488d9461..d2f3d8ee 100644 --- a/docs/reference_guides/transition_functions.md +++ b/docs/reference_guides/transition_functions.md @@ -7,9 +7,9 @@ The same transition functions work for all three estimators (CHS, AF, AMN) — t in `skillmodels.common.transition_functions` and are dispatched by name through each estimator's pipeline. CHS and AF support both the pre-built set and custom `@register_params` transitions. AMN also supports custom callables through its Stage-3 -generic nonlinear-least-squares path, but with narrower correction- and -fixed-parameter support than CHS (for example, `log_ces` / `log_ces_with_constant` -reject fixed parameters in that path). +generic nonlinear-least-squares path, but with narrower correction- and fixed-parameter +support than CHS (for example, `log_ces` / `log_ces_with_constant` reject fixed +parameters in that path). ## Pre-built Transition Functions @@ -33,17 +33,17 @@ $$ f_{t+1} = \sum_j \beta_j s_j + \sum_j \gamma_j s_j^2 + \sum_{j < k} \delta_{jk} s_j s_k + c $$ -Despite the name (convention in skill formation literature), this is not a true -translog function. +Despite the name (convention in skill formation literature), this is not a true translog +function. **Parameters**: Linear terms, squared terms, interaction terms, and constant. This is the general-library specification: parameters are enumerated over **all** -factors (latent and observed). An observed factor (e.g. income) therefore enters -the production function with its own free linear, square and interaction -coefficients. This is by design for the CHS estimator. In an AF production -function, observed factors must affect skills only through the investment -equation, so use `translog_af` (below) instead. +factors (latent and observed). An observed factor (e.g. income) therefore enters the +production function with its own free linear, square and interaction coefficients. This +is by design for the CHS estimator. In an AF production function, observed factors must +affect skills only through the investment equation, so use `translog_af` (below) +instead. ### translog_af @@ -58,11 +58,10 @@ For the canonical (skill, investment) pair this matches AF eq. (6), $a_t + g_1 \ln\theta + g_2 \ln I + g_3 \ln\theta \ln I$. Unlike the general `translog`, this function enumerates parameters over only the -production factors you pass to it. Use it for AF production so that observed -factors (e.g. income) cannot leak in as free production coefficients — pass only -the production factors (skill + investment) and keep observed factors out of the -production function. See [When to use the AF variants](#when-to-use-the-af-variants) -below. +production factors you pass to it. Use it for AF production so that observed factors +(e.g. income) cannot leak in as free production coefficients — pass only the production +factors (skill + investment) and keep observed factors out of the production function. +See [When to use the AF variants](#when-to-use-the-af-variants) below. **Parameters**: Linear terms, interaction terms, and constant (no squares). @@ -87,34 +86,33 @@ $$ f_{t+1} = \frac{1}{\phi} \ln\left(\sum_j \gamma_j e^{\phi \cdot s_j}\right) $$ -This is a KLS function—see [Notes on Factor Scales](../explanations/notes_on_factor_scales.md) -for implications. +This is a KLS function—see +[Notes on Factor Scales](../explanations/notes_on_factor_scales.md) for implications. **Parameters**: One weight $\gamma_j$ per factor (constrained to sum to 1) plus $\phi$. -This is the general-library specification: the CES weights $\gamma_j$ are -enumerated over **all** factors (latent and observed), so an observed factor -(e.g. income) receives a share of the probability simplex and enters the -production aggregate. This is by design for the CHS estimator. In an AF -production function, use `log_ces_af` (below) so the CES runs over the production -factors only. +This is the general-library specification: the CES weights $\gamma_j$ are enumerated +over **all** factors (latent and observed), so an observed factor (e.g. income) receives +a share of the probability simplex and enters the production aggregate. This is by +design for the CHS estimator. In an AF production function, use `log_ces_af` (below) so +the CES runs over the production factors only. ### log_ces_af -The AF (2020) production CES from equation (7): a log CES over the production -factors only. The math is identical to `log_ces`, +The AF (2020) production CES from equation (7): a log CES over the production factors +only. The math is identical to `log_ces`, $$ f_{t+1} = \frac{1}{\phi} \ln\left(\sum_j \gamma_j e^{\phi \cdot s_j}\right) $$ -but parameters are enumerated over only the production factors you pass to it, -not over observed factors. Use it for AF production (skill + investment) so that -observed factors cannot leak in as free CES weights — pass only the production -factors. See [When to use the AF variants](#when-to-use-the-af-variants) below. +but parameters are enumerated over only the production factors you pass to it, not over +observed factors. Use it for AF production (skill + investment) so that observed factors +cannot leak in as free CES weights — pass only the production factors. See +[When to use the AF variants](#when-to-use-the-af-variants) below. -**Parameters**: One weight $\gamma_j$ per production factor (constrained to sum -to 1) plus $\phi$ — the same set as `log_ces`. +**Parameters**: One weight $\gamma_j$ per production factor (constrained to sum to 1) +plus $\phi$ — the same set as `log_ces`. ### log_ces_general @@ -139,30 +137,28 @@ $$ ## When to use the AF variants -The general `translog` and `log_ces` (and the other built-in production -functions: `linear`, `robust_translog`, `linear_and_squares`, -`log_ces_with_constant`, `log_ces_general`) enumerate parameters over **all** -factors, including observed factors. For the CHS estimator this is the intended -behaviour: observed factors are allowed to enter the production function with -free coefficients. - -For an AF production function this is usually wrong. The AF model assumes that -observed factors (e.g. income) affect skills **only** through the investment -equation, not directly through production. Using a general built-in production -transition would give income its own free production coefficients, silently -changing the AF estimand. The `translog_af` and `log_ces_af` variants exist for -exactly this case: they take only the production factors (skill + investment) and -match AF equations (6) and (7) respectively. +The general `translog` and `log_ces` (and the other built-in production functions: +`linear`, `robust_translog`, `linear_and_squares`, `log_ces_with_constant`, +`log_ces_general`) enumerate parameters over **all** factors, including observed +factors. For the CHS estimator this is the intended behaviour: observed factors are +allowed to enter the production function with free coefficients. + +For an AF production function this is usually wrong. The AF model assumes that observed +factors (e.g. income) affect skills **only** through the investment equation, not +directly through production. Using a general built-in production transition would give +income its own free production coefficients, silently changing the AF estimand. The +`translog_af` and `log_ces_af` variants exist for exactly this case: they take only the +production factors (skill + investment) and match AF equations (6) and (7) respectively. To make the leakage visible, `validate_af_model` emits a loud `UserWarning` when a built-in production transition (`linear`, `linear_and_squares`, `translog`, -`robust_translog`, `log_ces`, `log_ces_with_constant`, `log_ces_general`) is used -on a non-endogenous production factor while observed factors are present. The -warning is not an error — intentionally-leaky models still run — but it flags the -wrong-estimand risk. Switch to `translog_af` / `log_ces_af`, or pin every -observed-factor transition coefficient to `0.0` via `fixed_params`, to remove the -leakage. (The endogenous investment equation legitimately uses observed factors, -so endogenous factors do not trigger the warning.) +`robust_translog`, `log_ces`, `log_ces_with_constant`, `log_ces_general`) is used on a +non-endogenous production factor while observed factors are present. The warning is not +an error — intentionally-leaky models still run — but it flags the wrong-estimand risk. +Switch to `translog_af` / `log_ces_af`, or pin every observed-factor transition +coefficient to `0.0` via `fixed_params`, to remove the leakage. (The endogenous +investment equation legitimately uses observed factors, so endogenous factors do not +trigger the warning.) ## Custom Transition Functions @@ -171,6 +167,7 @@ Define custom functions using the `@register_params` decorator: ```python from skillmodels.common.decorators import register_params + @register_params(params=["alpha", "beta"]) def my_transition(fac1, fac2, params): return params["alpha"] * fac1 + params["beta"] * fac2**2 @@ -181,9 +178,9 @@ def my_transition(fac1, fac2, params): Custom transition functions must: 1. Accept `params` as a mandatory argument (dictionary with registered parameter names) -2. Accept factor values as floats or use `states` for a JAX array of all factors -3. Return a float (or scalar JAX array) -4. Be JAX jit and vmap compatible (no Python control flow on state values) +1. Accept factor values as floats or use `states` for a JAX array of all factors +1. Return a float (or scalar JAX array) +1. 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tqdm ; extra == 'tqdm' + requires_python: '>=3.10' - pypi: https://files.pythonhosted.org/packages/fd/6c/173c7a3db77a6592210f73f194f0f8ed5e51b6ec61cfed7b1eee06ac5fd3/nvidia_cublas-13.6.0.2-py3-none-manylinux_2_27_x86_64.whl name: nvidia-cublas version: 13.6.0.2 diff --git a/pyproject.toml b/pyproject.toml index 05c5de79..d1e823b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ dependencies = [ "jupyter-book>=2", "kaleido>=1.2", "numpy>=2.4", - "optimagic>=0.5.3", + "optimagic>=0.5.4", "pandas>=3", "plotly>=6.6", "pytask>=0.5.8", @@ -48,6 +48,7 @@ email = "janos.gabler@gmail.com" [[project.maintainers]] name = "Janoś Gabler" email = "janos.gabler@gmail.com" + [[project.maintainers]] name = "Hans-Martin von Gaudecker" email = "hmgaudecker@uni-bonn.de" @@ -136,7 +137,6 @@ matplotlib = "*" # required because of pandas pandas-stubs = "*" types-pytz = "*" [tool.pixi.pypi-dependencies] -optimagic = { git = "https://github.com/optimagic-dev/optimagic.git", branch = "probability-allow-fixed-entries" } pdbp = "*" skillmodels = { path = ".", editable = true } [tool.pixi.workspace] @@ -156,6 +156,7 @@ unsafe-fixes = false select = [ "ALL" ] extend-ignore = [ "COM812", # Conflicts with ruff-format + "CPY001", # Missing copyright notice -- covered by the repo-level LICENSE file "EM101", # Exception must not use a string literal "EM102", # Exception must not use an f-string literal "FBT002", # Boolean default positional argument in function definition @@ -163,6 +164,7 @@ extend-ignore = [ "ISC001", # Conflicts with ruff-format "PERF401", # Use a list comprehension to create a transformed list "PLR0913", # Too many arguments in function definition + "PLR0917", # Too many positional arguments in function definition "PLR2004", # Magic value used in comparison "TC001", # Move application import into a type-checking block "TC002", # Move third-party import into a type-checking block @@ -191,7 +193,6 @@ pydocstyle.convention = "google" column_width = 88 max_supported_python = "3.14" table_format = "long" -collapse_tables = [ "tool.hatch", "tool.pytest", "tool.pytask", "tool.ty" ] expand_tables = [ "tool.pixi.dependencies", "tool.pixi.environments", @@ -215,10 +216,9 @@ expand_tables = [ "tool.pixi.feature.type-checking", "tool.pixi.feature.type-checking.pypi-dependencies", "tool.pixi.pypi-dependencies", - "tool.pixi.pypi-dependencies.optimagic", - "tool.pixi.pypi-dependencies.skillmodels", "tool.pixi.workspace", ] +collapse_tables = [ "tool.hatch", "tool.pytask", "tool.pytest", "tool.ty" ] [tool.ty] # ty resolves third-party imports from this pixi env (the official ty-pre-commit hook diff --git a/src/skillmodels/_beartype_conf.py b/src/skillmodels/_beartype_conf.py index 284182a6..835d8c80 100644 --- a/src/skillmodels/_beartype_conf.py +++ b/src/skillmodels/_beartype_conf.py @@ -64,7 +64,7 @@ def beartype_init[T](conf: BeartypeConf) -> Callable[[type[T]], type[T]]: """ def wrap(cls: type[T]) -> type[T]: - cls.__init__ = beartype(conf=conf)(cls.__init__) # ty: ignore[invalid-assignment] + cls.__init__ = beartype(conf=conf)(cls.__init__) return cls return wrap diff --git a/src/skillmodels/chs/likelihood_debug.py b/src/skillmodels/chs/likelihood_debug.py index ff22194c..6a77bc27 100644 --- a/src/skillmodels/chs/likelihood_debug.py +++ b/src/skillmodels/chs/likelihood_debug.py @@ -149,7 +149,7 @@ def log_likelihood( def _scan_body( carry: dict[str, Array], - loop_args: dict[str, Array], + loop_args: dict[str, Array | np.ndarray], controls: Array, parsed_params: ParsedParams, sigma_scaling_factor: float | Array, diff --git a/src/skillmodels/common/identification.py b/src/skillmodels/common/identification.py index 38cf4dd8..b171613b 100644 --- a/src/skillmodels/common/identification.py +++ b/src/skillmodels/common/identification.py @@ -335,11 +335,13 @@ def _scale_excess_message(factor_name: str, n_scale: int) -> list[str]: if n_scale <= 1: return [] return [ - f"Factor '{factor_name}' period 0: {n_scale} independent scale pins " - f"(loadings), but the initial scale orbit has one direction. " - f"{n_scale - 1} of them are testable restrictions, not normalizations -- " - f"they constrain identified features and can move the estimate under " - f"misspecification." + ( + f"Factor '{factor_name}' period 0: {n_scale} independent scale pins " + f"(loadings), but the initial scale orbit has one direction. " + f"{n_scale - 1} of them are testable restrictions, not normalizations -- " + f"they constrain identified features and can move the estimate under " + f"misspecification." + ) ] @@ -348,10 +350,12 @@ def _location_excess_message(factor_name: str, n_location: int) -> list[str]: if n_location <= 1: return [] return [ - f"Factor '{factor_name}' period 0: {n_location} independent location pins " - f"(measurement intercept and/or initial-component mean), but the initial " - f"location orbit has one direction. {n_location - 1} of them are testable " - f"restrictions, not normalizations." + ( + f"Factor '{factor_name}' period 0: {n_location} independent location pins " + f"(measurement intercept and/or initial-component mean), but the initial " + f"location orbit has one direction. {n_location - 1} of them are testable " + f"restrictions, not normalizations." + ) ] diff --git a/tests/test_cf_recovery.py b/tests/test_cf_recovery.py index 62254ad8..e340290c 100644 --- a/tests/test_cf_recovery.py +++ b/tests/test_cf_recovery.py @@ -220,15 +220,19 @@ def test_control_function_recovers_period_specific_kappa() -> None: noise = np.random.default_rng(SEED + 2).normal(scale=0.05, size=int(free.sum())) start.loc[free, "value"] = true_params.loc[free, "value"].to_numpy() + noise + import pandas as pd # noqa: PLC0415 + + def _neg_loglike_and_gradient(q: pd.DataFrame) -> tuple[float, np.ndarray]: + value, gradient = mi["loglike_and_gradient"](q) + return -value, -np.asarray(gradient) + res = om.minimize( fun=lambda q: -mi["loglike"](q), params=start[["value"]], algorithm="scipy_lbfgsb", bounds=om.Bounds(lower=start["lower_bound"], upper=start["upper_bound"]), constraints=mi["constraints"], - fun_and_jac=lambda q: tuple( - -np.asarray(v) for v in mi["loglike_and_gradient"](q) - ), + fun_and_jac=_neg_loglike_and_gradient, ) assert res.success est = res.params["value"] diff --git a/tests/test_correlation_heatmap.py b/tests/test_correlation_heatmap.py index df515409..8251b5f4 100644 --- a/tests/test_correlation_heatmap.py +++ b/tests/test_correlation_heatmap.py @@ -107,7 +107,9 @@ def test_get_factor_scores_data_with_single_period() -> None: ) data_std = data.iloc[:2][[f"y{i}" for i in range(4)]].copy(deep=True) for m in data_std.columns: - data_std[m] = (data_std[m] - np.mean(data_std[m])) / np.std(data_std[m], ddof=1) + data_std[m] = (data_std[m] - float(np.mean(data_std[m]))) / float( + np.std(data_std[m], ddof=1) + ) expected = pd.concat( [ data_std["y0"] / 2 + data_std["y2"] / 2, @@ -220,7 +222,9 @@ def test_get_factor_scores_data_with_multiple_period() -> None: data_std = data.iloc[:2][[f"y{i}" for i in range(4)]].copy(deep=True) for m in data_std.columns: - data_std[m] = (data_std[m] - np.mean(data_std[m])) / np.std(data_std[m], ddof=1) + data_std[m] = (data_std[m] - float(np.mean(data_std[m]))) / float( + np.std(data_std[m], ddof=1) + ) temp = ( pd.concat( [data_std["y0"] + data_std["y2"], data_std["y1"] + data_std["y3"]], @@ -233,7 +237,9 @@ def test_get_factor_scores_data_with_multiple_period() -> None: data_std = data.iloc[2:][[f"y{i}" for i in range(4)]].copy(deep=True) for m in data_std.columns: - data_std[m] = (data_std[m] - np.mean(data_std[m])) / np.std(data_std[m], ddof=1) + data_std[m] = (data_std[m] - float(np.mean(data_std[m]))) / float( + np.std(data_std[m], ddof=1) + ) temp = ( pd.concat( [data_std["y0"] + data_std["y2"], data_std["y1"] + data_std["y3"]],