Automatic Lag Selection in Covariance Matrix EstimationKenneth D. West, Whitney K. Newey
NBER Technical Working Paper No. 144 We 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, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions. Published:
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