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Automatic Lag Selection in Covariance Matrix Estimation

Automatic Lag Selection in Covariance Matrix Estimation 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. This content is only available as a PDF. © 1994 The Review of Economic Studies Limited http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Review of Economic Studies Oxford University Press

Automatic Lag Selection in Covariance Matrix Estimation

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References (18)

Publisher
Oxford University Press
Copyright
© 1994 The Review of Economic Studies Limited
ISSN
0034-6527
eISSN
1467-937X
DOI
10.2307/2297912
Publisher site
See Article on Publisher Site

Abstract

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. This content is only available as a PDF. © 1994 The Review of Economic Studies Limited

Journal

The Review of Economic StudiesOxford University Press

Published: Oct 1, 1994

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