01785naa a2200193 a 450000100080000000500110000800800410001910000220006024500840008226000090016652012590017565000160143465300190145065300160146965300240148565300090150965300210151877300520153914179341994-03-07       1991    bl ---       0--  u    #d1 aANDREWS, D. W. K.  aHeteroskedasticity and autocorrelation consistent covariance matrix estimation.  c1991  aThis paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators they are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme. Results in the literature provide a condition on the growth rate of the lag truncation parameter as T -&gt oo that is sufficient for consistency. No results are available, however, regarding the choice of lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of weighting scheme. In consequence, available estimators are not entirely operational and the relative merits of the estimators are unknown. This paper addresses these problems. The asymtotic truncated mean squared errors of estimators in a given class are determined and compared. Asymptotically optimal kernel/weighting scheme and bandwidth/lag truncation parameters are obtained using an asymptotic truncated mean swuared error criterion. Using these results, data-dependent automatic bandwidth/lag truncation parameters are introduced. The finite sampleproperties of the estimators are analyzed via Monte Carlo simulation.  aEconometria  aAntocorrelacao  aCovariancia  aDensidade espectral  aErro  aSeries temporais  tEconometricagv.59, n.3, p.817-858, May., 1991.