bayespecon.models.OLSPanelFE¶

class bayespecon.models.OLSPanelFE(formula=None, data=None, y=None, X=None, W=None, unit_col=None, time_col=None, N=None, T=None, model=0, priors=None, logdet_method=None, robust=False, w_vars=None, backend=None)[source]¶

Bayesian pooled and fixed-effects linear panel regression.

Implements the Gaussian panel model

\[y_{it} = x_{it}'\beta + \alpha_i + \tau_t + \varepsilon_{it}, \qquad \varepsilon_{it} \sim \mathcal{N}(0, \sigma^2),\]

where the included effects depend on model: 0 pooled, 1 unit effects, 2 time effects, 3 two-way effects. The within transformation is handled by SpatialPanelModel before the likelihood is evaluated.

Parameters:¶

formula : str, optional¶

Wilkinson-style formula, e.g. "y ~ x1 + x2". Requires data, unit_col, and time_col.

data : pandas.DataFrame, optional¶

Long-format panel data when using formula mode.

y : array-like, optional¶

Stacked response of shape (N*T,) in unit-major order. Required in matrix mode.

X : array-like or pandas.DataFrame, optional¶

Stacked design matrix of shape (N*T, k). Required in matrix mode. DataFrame columns are preserved as feature names.

W : libpysal.graph.Graph or scipy.sparse matrix¶

Spatial weights of shape (N, N) (preferred) or (N*T, N*T) block-diagonal. Accepted for API consistency with the other panel models but does not enter the OLS likelihood; required if downstream Bayesian LM diagnostics will be run.

unit_col : str, optional¶

Column in data identifying the cross-sectional unit. Required in formula mode.

time_col : str, optional¶

Column in data identifying the time period. Required in formula mode.

N : int, optional¶

Number of cross-sectional units. Required in matrix mode if not inferable.

T : int, optional¶

Number of time periods. Required in matrix mode if not inferable.

model : int, default 0¶

Fixed-effects specification: 0 pooled, 1 unit FE, 2 time FE, 3 two-way FE.

priors : dict, optional¶

Override default priors. Supported keys:

beta_mu (float, default 0.0): Normal prior mean for \(\beta\).
beta_sigma (float, default 1e6): Normal prior std for \(\beta\).
sigma_sigma (float, default 10.0): HalfNormal prior std for \(\sigma\).
nu_lam (float, default 1/30): Rate of TruncExp(lower=2) prior on \(\nu\) (only used when robust=True).

logdet_method : str, optional¶

Accepted for API consistency; unused in OLSPanelFE (no spatial Jacobian).

robust : bool, default False¶

If True, replace the Normal error with Student-t. See Robust regression below.

Notes

This is the aspatial baseline for panel LM diagnostics and panel model comparison. The spatial weights object W is accepted for API consistency but does not enter the likelihood.

Robust regression

When robust=True, the error distribution is changed from Normal to Student-t, yielding a model that is robust to heavy-tailed outliers:

\[\varepsilon_{it} \sim t_\nu(0, \sigma^2)\]

where \(\nu \sim \mathrm{TruncExp}(\lambda_\nu, \mathrm{lower}=2)\) with rate nu_lam (default 1/30). The default nu_lam = 1/30 gives a prior mean of approximately 30, favouring near-Normal tails. The lower bound of 2 ensures the variance exists.

__init__(formula=None, data=None, y=None, X=None, W=None, unit_col=None, time_col=None, N=None, T=None, model=0, priors=None, logdet_method=None, robust=False, w_vars=None, backend=None)[source]¶

Methods

`__init__`([formula, data, y, X, W, unit_col, ...])
`fit`([draws, tune, chains, target_accept, ...])	Draw samples from the posterior.
`fitted_values`()	Return fitted values at posterior mean parameters.
`residuals`()	Return residuals on the observed (or transformed-panel) scale.
`spatial_diagnostics`()	Run Bayesian LM specification tests and return a summary table.
`spatial_diagnostics_decision`([alpha, ...])	Return a model-selection decision from Bayesian LM test results.
`spatial_effects`([return_posterior_samples])	Compute Bayesian inference for direct, indirect, and total impacts.
`summary`([var_names])	Return posterior summary table.

Attributes

`inference_data`	Return the ArviZ InferenceData from the most recent fit.
`pymc_model`	Return the PyMC model object built for the most recent fit.

fit(draws=2000, tune=1000, chains=4, target_accept=0.9, random_seed=None, **sample_kwargs)[source]¶

Draw samples from the posterior.

Parameters:¶

draws : int¶: Number of posterior samples per chain (after tuning).
tune : int¶: Number of tuning (burn-in) steps per chain.
chains : int¶: Number of parallel chains.
target_accept : float¶: Target acceptance rate for NUTS.
random_seed : int, optional¶: Seed for reproducibility.
**sample_kwargs¶: Additional keyword arguments forwarded to pm.sample. Pass nuts_sampler="blackjax" (or "numpyro", "nutpie") to select an alternative NUTS backend; defaults to PyMC’s built-in sampler.

Return type:¶

arviz.InferenceData

fitted_values()[source]¶: Return fitted values at posterior mean parameters.

property inference_data : arviz.data.inference_data.InferenceData | None[source]¶: Return the ArviZ InferenceData from the most recent fit.

property pymc_model : pymc.model.core.Model | None[source]¶: Return the PyMC model object built for the most recent fit.

residuals()[source]¶: Return residuals on the observed (or transformed-panel) scale.

spatial_diagnostics()[source]¶

Run Bayesian LM specification tests and return a summary table.

Iterates over the class-level _spatial_diagnostics_tests registry and calls each test function on this fitted model, collecting the results into a tidy DataFrame. The set of tests depends on the model type.

Requires the model to have been fit (.fit() called). For cross-sectional models a spatial weights matrix W must also have been supplied at construction time.

Returns:¶

DataFrame indexed by test name with columns statistic (posterior mean), median, df (degrees of freedom for the \(\chi^2\) reference), p_value (Bayesian p-value 1 - chi2.cdf(mean, df)), and ci_lower / ci_upper (95% credible interval). The DataFrame carries attrs["model_type"] and attrs["n_draws"] metadata.

Return type:¶

pandas.DataFrame

Raises:¶

RuntimeError – If the model has not been fit yet.
ValueError – If a cross-sectional model was constructed without W.