statsmodels.stats.sandwich_covariance.cov_hac
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statsmodels.stats.sandwich_covariance.cov_hac(results, nlags=None, weights_func=, use_correction=True)
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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.
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