bayespecon.models.flow_panel.SARFlowSeparablePanel

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

Panel separable spatial-lag flow model with \(\rho_w = -\rho_d \rho_o\).

For each period \(t\),

\[y_t = \rho_d W_d y_t + \rho_o W_o y_t - \rho_d \rho_o W_w y_t + X_t \beta + \varepsilon_t, \qquad \varepsilon_t \sim \mathcal{N}(0, \sigma^2 I_N).\]

Under the separability restriction, \(A = I_N - \rho_d W_d - \rho_o W_o + \rho_d \rho_o W_w\) factorizes into Kronecker blocks, which enables the exact or approximated eigenvalue-based log-determinant used by this class.

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

Methods

__init__(y, G, X, **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.