varwg.time_series_analysis.models.VAR_order_selection

varwg.time_series_analysis.models.VAR_order_selection(data, p_max=10, criterion=<function SC>, estimator=<function VAR_LS>, est_kwds=None)

Order selection for VAR processes to allow parsimonious parameterization.

Parameters:

data(K, T) ndarray

Input data with K variables and T timesteps.

p_maxint, optional

Maximum number of autoregressive order to evaluate.

criterionfunction, optional

Information criterion that accepts sigma_u, p, and T and returns something that gives small values for a parsimonious set of these parameters.

Returns:

pint

Suggested autoregressive order.

See also

AIC

Akaike Information criterion

FPE

Final Prediction Error

HQ

Hannan-Quinn information criterion

SC

Schwartz Criterion

VAR_LS

Least-squares estimator.

VAR_residuals

Returns the residuals based on given data and LS estimator

VAR_LS_sim

Simulation based on LS estimator.

VAR_LS_predict

Predict given prior data and LS estimator.