bayespecon.models.panel_base.SpatialPanelModel¶

class bayespecon.models.panel_base.SpatialPanelModel(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]¶

Base class for static spatial panel models with FE transforms.

Parameters:¶

formula=None¶: Either formula mode (formula + data) or matrix mode (y + X).
data=None¶: Either formula mode (formula + data) or matrix mode (y + X).
y=None¶: Either formula mode (formula + data) or matrix mode (y + X).
X=None¶: Either formula mode (formula + data) or matrix mode (y + X).
W : libpysal.graph.Graph or scipy.sparse matrix¶: Spatial weights of shape (N, N) (preferred) or the full (N*T, N*T) block-diagonal panel matrix. Accepts a libpysal.graph.Graph (the modern libpysal graph API) or any scipy.sparse matrix. The legacy libpysal.weights.W object is not accepted directly; pass w.sparse to use the underlying sparse matrix, or convert with libpysal.graph.Graph.from_W(w). W should be row-standardised; a UserWarning is raised if not.
unit_col=None¶: Required in formula mode for robust panel sorting and N/T inference.
time_col=None¶: Required in formula mode for robust panel sorting and N/T inference.
N=None¶: Required in matrix mode if not inferable.
T=None¶: Required in matrix mode if not inferable.
model=0¶: 0 pooled, 1 unit FE, 2 time FE, 3 two-way FE.
priors : dict, optional¶: Override default priors. Keys depend on the model subclass; see each model’s docstring for supported keys.
logdet_method : str, optional¶: How to compute log|I - rho*W|. When None, the method is auto-selected from the cross-sectional N x N weights size via bayespecon.logdet._auto_logdet_method(): "eigenvalue" for N <= 2000 and "chebyshev" otherwise.
robust : bool, default False¶: If True, use a Student-t error distribution instead of Normal, yielding a model that is robust to heavy-tailed outliers. When robust=True, a nu (degrees of freedom) parameter is added to the model with an Exponential(lam=nu_lam) prior (default nu_lam = 1/30, mean ≈ 30). The nu prior can be controlled via the priors dict with key nu_lam.

__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 the posterior for the panel model.
`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, **sample_kwargs)[source]¶

Sample the posterior for the panel model.

Parameters:¶

draws : int, default=2000¶: Number of post-tuning draws per chain.
tune : int, default=1000¶: Number of tuning draws per chain.
chains : int, default=4¶: Number of chains.
target_accept : float, default=0.9¶: NUTS target acceptance probability.
random_seed : int, optional¶: Random seed used by PyMC.
**sample_kwargs¶: Extra keyword arguments forwarded to pymc.sample().

Returns:¶

Posterior samples and diagnostics.

Return type:¶

arviz.InferenceData

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.