Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator

A T̓ consistent estimator of a heteroskedasticity and autocorrelation consistent covariance matrix estimator is proposed and evaluated. The relevant applications are ones in which the regression disturbance follows a moving average process of known order. In a system of ̓equations, this `MA-'̓ estimator entails estimation of the moving average coefficients of an -̓dimensional vector. Simulations indicate that the MA- ̓estimator's finite sample performance is better than that of the estimators of Andrews and Monahan (1992) and Newey and West (1994) when cross-products of instruments and disturbances are sharply negatively autocorrelated, comparable or slightly worse otherwise.