bayespecon.models.SDEMPanelFE¶

class bayespecon.models.SDEMPanelFE(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)[source]¶

Bayesian spatial Durbin error panel regression.

Implements

\[\begin{split}y_{it} = x_{it}'\\beta + W x_{it}'\\theta + \\alpha_i + \\tau_t + u_{it}, \\qquad u_{it} = \\lambda W u_{it} + \\varepsilon_{it}, \\qquad \\varepsilon_{it} \\sim \\mathcal{N}(0, \\sigma^2).\end{split}\]

The sampled coefficient vector stacks the local and lagged-covariate blocks as \([\\beta, \\theta]\).

Parameters:¶

formula=None¶
data=None¶
y=None¶
X=None¶
W=None¶
unit_col=None¶
time_col=None¶
N=None¶
T=None¶
model=0¶
priors=None¶

:param : :param logdet_method: See SpatialPanelModel. :param **Robust regression**: :param When robust=True: :param the spatially filtered error distribution is: :param changed from Normal to Student-t: :param yielding a model that is robust to: :param heavy-tailed outliers: :param .. math::: \varepsilon_{it} = (I - \lambda W)(y - X\beta_1 - WX\beta_2 - \mu_i) \sim t_\nu(0, \sigma^2) :param where \(\\nu \\sim \\mathrm{TruncExp}(\\lambda_\\nu: :param \\mathrm{lower}=2)\) with rate nu_lam (default 1/30).: :param The default nu_lam = 1/30 gives a prior mean of approximately 30: :param : :param favouring near-Normal tails. The lower bound of 2 ensures the: :param 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)[source]¶

Methods

`__init__`([formula, data, y, X, W, unit_col, ...])
`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.

The SDEM panel model uses pm.Potential for both the Gaussian error log-likelihood and the Jacobian, so neither is auto-captured. We compute the complete pointwise log-likelihood manually after sampling, using eigenvalue-based Jacobian for efficiency.

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.