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_kwdsdict

Unused

resetint, 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_tbool, optional

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

params_onlybool, optional

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

Returns:
RollingRegressionResults

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

Last update: Dec 16, 2024