Automatic Lag Selection in Covariance Matrix Estimation

Abstract

The authors propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, they prove that their procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations suggest that the authors' procedure performs tolerably well, although it does result in size distortions. Copyright 1994 by The Review of Economic Studies Limited.