Automatic Lag Selection in Covariance Matrix Estimation

51 Pages Posted: 18 Jul 2000 Last revised: 25 Nov 2022

See all articles by Kenneth D. West

Kenneth D. West

University of Wisconsin - Madison - Department of Economics; National Bureau of Economic Research (NBER)

Whitney K. Newey

Massachusetts Institute of Technology (MIT) - Department of Economics; National Bureau of Economic Research (NBER)

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

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

Kenneth D. West (Contact Author)

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Whitney K. Newey

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

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