stats.sandwich_covariance.cov_hac()

statsmodels.stats.sandwich_covariance.cov_hac

statsmodels.stats.sandwich_covariance.cov_hac(results, nlags=None, weights_func=<function weights_bartlett>, use_correction=True)

heteroscedasticity and autocorrelation robust covariance matrix (Newey-West)

Assumes we have a single time series with zero axis consecutive, equal spaced time periods

Parameters:	results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead nlags : int or None highest lag to include in kernel window. If None, then nlags = floor[4(T/100)^(2/9)] is used. weights_func : callable weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights
Returns:	cov : ndarray, (k_vars, k_vars) HAC robust covariance matrix for parameter estimates

Notes

verified only for nlags=0, which is just White just guessing on correction factor, need reference

options might change when other kernels besides Bartlett are available.