bayespecon.models.OLSPanelDynamic¶

class bayespecon.models.OLSPanelDynamic(*args, model=0, **kwargs)[source]¶

__init__(*args, model=0, **kwargs)[source]¶

Methods

`__init__`(*args[, model])
`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.