statsmodels.regression.rolling.RollingWLS.fit¶

RollingWLS.fit(method='inv', cov_type='nonrobust', cov_kwds=None, reset=None, use_t=False, params_only=False)[source]¶

Estimate model parameters.

Parameters:¶

method : {'inv', 'lstsq', 'pinv'}¶

Method to use when computing the the model parameters.

• ’inv’ - use moving windows inner-products and matrix inversion. This method is the fastest, but may be less accurate than the other methods.

• ’lstsq’ - Use numpy.linalg.lstsq

• ’pinv’ - Use numpy.linalg.pinv. This method matches the default estimator in non-moving regression estimators.

cov_type : {'nonrobust', 'HCCM', 'HC0'}¶

Covariance estimator:

• nonrobust - The classic OLS covariance estimator

• HCCM, HC0 - White heteroskedasticity robust covariance

cov_kwds : dict¶

Unused

reset : int, optional¶

Interval to recompute the moving window inner products used to estimate the model parameters. Smaller values improve accuracy, although in practice this setting is not required to be set.

use_t : bool, optional¶

Flag indicating to use the Student’s t distribution when computing p-values.

params_only : bool, optional¶

Flag indicating that only parameters should be computed. Avoids calculating all other statistics or performing inference.

Returns:¶

Estimation results where all pre-sample values are nan-filled.

Return type:¶

RollingRegressionResults