HAC-type standard errors

HAC-type standard errors refer to standard errors that are estimated using Heteroskedasticity and Autocorrelation Consistent (HAC) methods.

In econometrics, HAC methods are used when the data violate the assumptions of homoscedasticity and independence. When these assumptions are violated, the standard errors estimated using ordinary least squares (OLS) regression may be biased and inefficient.

HAC methods involve estimating the covariance matrix of the errors in the regression model, taking into account the presence of heteroskedasticity and autocorrelation. The resulting HAC estimator is robust to these violations of the assumptions, and the standard errors estimated using the HAC estimator are more reliable than those estimated using OLS.

HAC-type standard errors are commonly used in time-series and panel data analyses to account for autocorrelation and heteroskedasticity in the data.