bayespecon.SDEMPanelRE¶

class bayespecon.SDEMPanelRE(**kwargs)[source]¶

Bayesian spatial Durbin error panel model with unit random effects.

\[y_{it} = X_{it}\beta + (WX)_{it}\theta + \alpha_i + u_{it}, \quad u_{it} = \lambda (Wu)_{it} + \varepsilon_{it}\]

Combines the SDEM mean structure (covariates plus their spatial lags) with random unit effects \(\alpha_i \sim N(0, \sigma_\alpha^2)\) and a spatially-correlated error term governed by \(\lambda\).

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,). Required in matrix mode.

X : array-like or pandas.DataFrame, optional

Stacked design matrix. Required in matrix mode.

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

Spatial weights of shape (N, N). Used to construct the WX block and the spatial filter on the disturbance. Should be row-standardised.

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.

T : int, optional

Number of time periods. Required in matrix mode.

priors : dict, optional

Override default priors. Supported keys:

lam_lower (float, default -1.0): Lower bound of Uniform prior on \(\lambda\).
lam_upper (float, default 1.0): Upper bound of Uniform prior on \(\lambda\).
beta_mu (float, default 0.0): Normal prior mean for \([\beta, \theta]\).
beta_sigma (float, default 1e6): Normal prior std for \([\beta, \theta]\).
sigma_sigma (float, default 10.0): HalfNormal prior std for \(\sigma\).
sigma_alpha_sigma (float, default 10.0): HalfNormal prior std for \(\sigma_\alpha\).
nu_lam (float, default 1/30): Rate of TruncExp(lower=2) prior on \(\nu\) (only used when robust=True).

logdet_method : str, optional

How to compute \(\log|I - \lambda W|\); auto-selected when None (default).

robust : bool, default False

If True, replace the Normal innovation with Student-t.

w_vars : list of str, optional

Names of X columns to spatially lag. By default all non-constant columns are lagged. At least one column must be lagged; if no WX columns remain a ValueError is raised. Pass a subset to restrict which variables receive a spatial lag.

Notes

The base-class model argument is not exposed; pooled mean structure (model=0) is used because unit heterogeneity is captured by the random effect rather than by within-unit demeaning.

__init__(**kwargs)[source]¶

Methods

`__init__`(**kwargs)
`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, format])	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.

Looks up the diagnostic suite registered for this model class 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.