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
resultinstance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead
- nlags
intorNone 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
- results
- Returns:
- cov
ndarray, (k_vars,k_vars) HAC robust covariance matrix for parameter estimates
- cov
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.