*, cov_type='nonrobust', cov_kwds=None, use_t=True)[source]

Estimate the model parameters.


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

  • ‘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


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’.


Estimation results.

See also


Ordinary Least Squares estimation.


Ordinary Least Squares estimation.


See get_robustcov_results for a detailed list of available covariance estimators and options.


Use OLS to estimate model parameters and to estimate parameter covariance.

Last update: Jul 16, 2024