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