bayespecon.models.flow_panel.OLSFlowPanel¶

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

Non-spatial Bayesian OD-flow gravity model for balanced panel data.

Panel analogue of OLSFlow: implements the conventional log-linear gravity specification of Thomas-Agnan and LeSage [2014] (eq. 83.2) with no spatial lag terms,

\[y_{t} = X_{t}\,\beta + \varepsilon_{t}, \quad \varepsilon_{t} \sim \mathcal{N}(0, \sigma^{2} I_{N}),\]

on a balanced panel of \(T\) periods, applying the same fixed-effects within transform (model argument) as the spatial panel flow models. Provided as the canonical null model for Bayesian LM diagnostics on panel flow data.

Parameters:¶

y : array-like¶

Stacked panel response in shape (T, n, n), (T, n^2), or (n^2 * T,).

G : libpysal.graph.Graph¶

Row-standardised graph on n units. Required for API symmetry but not used in estimation.

X¶

Stacked panel design matrix in time-first order.

T : int¶

Number of panel periods.

col_names : list of str, optional

Feature names for X. Inferred from a DataFrame if omitted.

k : int, optional

Number of destination/origin covariate pairs used by flow effects; inferred from columns prefixed dest_ if omitted.

model : int, default 0

Fixed-effects transform: 0 pooled, 1 pair FE, 2 time FE, 3 two-way FE.

priors : dict, optional

Override default priors. Supported keys:

beta_mu (float, default 0.0): Normal prior mean for \(\beta\).
beta_sigma (float, default 1e6): Normal prior std for \(\beta\).
sigma_sigma (float, default 10.0): HalfNormal prior std for \(\sigma\).
nu_lam (float, default 1/30): Rate of TruncExp(lower=2) prior on \(\nu\) (only used when robust=True).

Spatial keys (rho_*) are ignored in this aspatial baseline.

robust : bool, default False

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

symmetric_xo_xd : bool, optional

Whether to constrain origin and destination covariate effects to be equal. Forwarded to FlowPanelModel.

Notes

All log-determinant precomputation is skipped (A = I_N with \(|A| = 1\)).

__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 flows for the OLS panel gravity model.
`spatial_diagnostics`()	Run Bayesian LM specification tests for flow panel models.
`spatial_diagnostics_decision`([alpha, ...])	Return a model-selection decision from Bayesian LM test results.
`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, parallel=-1)[source]¶

Draw posterior-predictive flows for the OLS panel gravity model.

Overrides the base implementation, which expects rho_d, rho_o, rho_w posterior arrays that this model does not sample.

property pymc_model : pymc.model.core.Model | None[source]¶: Return the most recently built PyMC model.

spatial_diagnostics()[source]¶

Run Bayesian LM specification tests for flow panel models.

See bayespecon.models.base.SpatialModel.spatial_diagnostics() for the column schema.

Raises:¶: RuntimeError – If the model has not been fit yet.

spatial_diagnostics_decision(alpha=0.05, format='graphviz', theme='default')[source]¶

Return a model-selection decision from Bayesian LM test results.

Walks the panel-flow decision tree using Bayesian p-values from spatial_diagnostics() and recommends either OLSFlowPanel (no spatial dependence detected) or SARFlowPanel (at least one direction is significant).

Parameters:¶

alpha : float, default 0.05¶: Significance level for the Bayesian p-values.
format : {"graphviz", "ascii", "model"}, default "graphviz"¶: Output format. "model" returns the recommended model name string. "ascii" returns an indented box-drawing tree. "graphviz" returns a graphviz.Digraph (with ASCII fallback if graphviz is not installed).

Return type:¶

str or graphviz.Digraph

spatial_effects(draws=None, return_posterior_samples=False, ci=0.95, mode='auto', parallel=-1)[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) and the parallel kwarg.

summary(var_names=None, **kwargs)[source]¶: Return posterior summary table via ArviZ.