Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator

   (695 K)

A þT 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 þ equations, this `MA-þ' estimator entails estimation of the moving average coefficients of an þ-dimensional vector. Simulations indicate that the MA-þ 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.

Published: West, Kenneth D. "Another Heteroskedasticity- And Autocorrelation-Consistent Covariance Matrix Estimator," Journal of Econometrics, 1997, v76(1&2,Jan/Feb), 171-191.

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