Another heteroskedasticity- and autocorrelation-consistent covariance matrix estimator

A ✓T-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 l equations, this ‘AM-l’ estimator entails estimation of the moving average coefficients of an l-dimensional vector. Simulations indicate that the MA-l estimatorʹs 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.