Automatic Lag Selection in Covariance Matrix Estimation
51 Pages Posted: 18 Jul 2000 Last revised: 26 May 2023
Date Written: February 1995
Abstract
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.
Suggested Citation: Suggested Citation
West, Kenneth D. and Newey, Whitney K., Automatic Lag Selection in Covariance Matrix Estimation (February 1995). NBER Working Paper No. t0144, Available at SSRN: https://ssrn.com/abstract=225129
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