bayespecon.models.flow_panel.SEMFlowPanel¶

class bayespecon.models.flow_panel.SEMFlowPanel(y, G, X, T, **kwargs)[source]¶

Panel spatial-error flow model with three free spatial parameters.

Panel analogue of SEMFlow: applies the Kronecker spatial filter (\(W_d\), \(W_o\), \(W_w\)) to the disturbance rather than the dependent variable, period by period:

\[y_t = X_t \beta + u_t, \qquad B u_t = \varepsilon_t, \quad \varepsilon_t \sim \mathcal{N}(0, \sigma^2 I_N).\]

The Jacobian contribution scales as \(T \cdot \log|B|\) — identical in form to SARFlowPanel. Marginal mean is \(X_t \beta\), so there are no \(X\)-mediated spillovers; effects collapse to the closed-form expressions used by OLSFlowPanel.

__init__(y, G, X, T, **kwargs)[source]¶

Methods

__init__(y, G, X, T, **kwargs)
fit([draws, tune, chains, target_accept, ...])	Draw samples from the posterior.
fit_approx([draws, n, method, random_seed, ...])	Fit a variational approximation and return posterior draws.
posterior_predictive([n_draws, random_seed])	Draw posterior-predictive samples y_rep for the full panel stack.
spatial_effects([draws, ...])	Summarise posterior origin/destination/intra/network/total effects.
summary([var_names])	Return posterior summary table via ArviZ.

Attributes

approximation	Return the most recent PyMC variational approximation, if any.
inference_data	Return posterior draws from the most recent fit.
pymc_model	Return the most recently built PyMC model.

property approximation[source]¶

Return the most recent PyMC variational approximation, if any.

fit(draws=2000, tune=1000, chains=4, target_accept=0.9, random_seed=None, store_lambda=False, idata_kwargs=None, **sample_kwargs)[source]¶

Draw samples from the posterior.

fit_approx(draws=2000, n=10000, method='advi', random_seed=None, store_lambda=False, compute_log_likelihood=True, **fit_kwargs)[source]¶

Fit a variational approximation and return posterior draws.

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

Return posterior draws from the most recent fit.

posterior_predictive(n_draws=None, random_seed=None)[source]¶

Draw posterior-predictive samples y_rep for the full panel stack.

Parameters:¶

n_draws : int, optional¶

Number of posterior draws to use. Defaults to all.

random_seed : int, optional¶

Seed for the noise/Poisson sampler.

Returns:¶

Array of shape (n_draws, N_flow * T) with posterior-predictive flows in time-first stacked order.

Return type:¶

np.ndarray

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

Return the most recently built PyMC model.

spatial_effects(draws=None, return_posterior_samples=False, ci=0.95, mode='auto')[source]¶

Summarise posterior origin/destination/intra/network/total effects.

See bayespecon.models.flow.FlowModel.spatial_effects() for the mode semantics (auto / combined / separate destination-origin sides per Thomas-Agnan & LeSage 2014, §83.5.2).

summary(var_names=None, **kwargs)[source]¶

Return posterior summary table via ArviZ.