statsmodels.tsa.ar_model.AutoReg.fit¶

AutoReg.fit(cov_type='nonrobust', cov_kwds=None, use_t=False)[source]¶

Estimate the model parameters.

Parameters:¶

cov_typestr

The covariance estimator to use. The most common choices are listed below. Supports all covariance estimators that are available in OLS.fit.

‘nonrobust’ - The class OLS covariance estimator that assumes homoskedasticity.
‘HC0’, ‘HC1’, ‘HC2’, ‘HC3’ - Variants of White’s (or Eiker-Huber-White) covariance estimator. HC0 is the standard implementation. The other make corrections to improve the finite sample performance of the heteroskedasticity robust covariance estimator.
‘HAC’ - Heteroskedasticity-autocorrelation robust covariance estimation. Supports cov_kwds.
- maxlags integer (required) : number of lags to use.
- kernel callable or str (optional)kernel
  currently available kernels are [‘bartlett’, ‘uniform’], default is Bartlett.
- use_correction bool (optional)If true, use small sample
  correction.

cov_kwdsdict, optional

A dictionary of keyword arguments to pass to the covariance estimator. nonrobust and HC# do not support cov_kwds.

use_tbool, optional

A flag indicating that inference should use the Student’s t distribution that accounts for model degree of freedom. If False, uses the normal distribution. If None, defers the choice to the cov_type. It also removes degree of freedom corrections from the covariance estimator when cov_type is ‘nonrobust’.

Returns:¶

AutoRegResults: Estimation results.