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Automatic Lag Selection in Covariance Matrix Estimation

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Author Info
Kenneth D. West
Whitney K. Newey

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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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0144.

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Date of creation: Feb 1995
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Handle: RePEc:nbr:nberte:0144

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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This page was last updated on 2009-11-19.


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