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Automatic Lag Selection in Covariance Matrix Estimation

Опубликовано на портале: 13-04-2004
The Review of Economic Studies. 1994.  Vol. 61. No. 4. P. 631-653. 
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Authors proposes 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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