bayespecon.models.SEMPanelDynamic¶

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

Dynamic spatial-error panel regression.

Implements

\[\begin{split}y_{it} = \\phi y_{i,t-1} + x_{it}'\\beta + u_{it}, \\qquad u_{it} = \\lambda W u_{it} + \\varepsilon_{it}, \\qquad \\varepsilon_{it} \\sim \\mathcal{N}(0, \\sigma^2).\end{split}\]

The likelihood uses the filtered residuals \((I - \\lambda W)(y_t - \\phi y_{t-1} - X_t \\beta)\) and the associated Jacobian \(|I - \\lambda W|^{T-1}\).

Robust regression

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

\[\begin{split}\\varepsilon_t = (I - \\lambda W)(y_t - \\phi y_{t-1} - X_t \\beta) \\sim t_\\nu(0, \\sigma^2)\end{split}\]

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__(*args, model=0, **kwargs)[source]¶

Methods

`__init__`(*args[, model])
`fit`([draws, tune, chains, target_accept, ...])	Sample posterior and attach pointwise log-likelihood for IC metrics.
`fitted_values`()	Return fitted values at posterior mean parameters.
`residuals`()	Return transformed residuals `y - fitted`.
`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, idata_kwargs=None, **sample_kwargs)[source]¶: Sample posterior and attach pointwise log-likelihood for IC metrics.

fitted_values()[source]¶

Return fitted values at posterior mean parameters.

Returns:¶: Fitted values on transformed panel scale.
Return type:¶: np.ndarray

property inference_data : arviz.data.inference_data.InferenceData | None[source]¶

Return the ArviZ InferenceData from the most recent fit.

Returns:¶: The inference data object, or None if the model has not been fit yet.
Return type:¶: arviz.InferenceData or None

property pymc_model : pymc.model.core.Model | None[source]¶

Return the PyMC model object built for the most recent fit.

Returns:¶: The model object used by fit(), or None if the instance has not been fit yet.
Return type:¶: pymc.Model or None

residuals()[source]¶

Return transformed residuals y - fitted.

Returns:¶: Residual vector on transformed panel scale.
Return type:¶: np.ndarray

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 — for example, an OLSPanelFE model runs Panel-LM-Lag, Panel-LM-Error, Panel-LM-SDM-Joint, and Panel-LM-SLX-Error-Joint.

Requires the model to have been fit (.fit() called) and a spatial weights matrix W to have been supplied at construction time.

Returns:¶

DataFrame indexed by test name with columns:

Column	Description
statistic	Posterior mean of the LM statistic
median	Posterior median of the LM statistic
df	Degrees of freedom for the \(\chi^2\) reference
p_value	Bayesian p-value: `1 - chi2.cdf(mean, df)`
ci_lower	Lower bound of 95% credible interval (2.5%)
ci_upper	Upper bound of 95% credible interval (97.5%)

The DataFrame has attrs["model_type"] (class name) and attrs["n_draws"] (total posterior draws) metadata.

Return type:¶

pandas.DataFrame

Raises:¶

RuntimeError – If the model has not been fit yet.