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A TWO-STAGE PLUG-IN BANDWIDTH SELECTION AND ITS IMPLEMENTATION FOR COVARIANCE ESTIMATION

Published online by Cambridge University Press:  26 October 2009

Masayuki Hirukawa
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Abstract

The two most popular bandwidth choice rules for kernel HAC estimation have been proposed by Andrews (1991) and Newey and West (1994). This paper suggests an alternative approach that estimates an unknown quantity in the optimal bandwidth for the HAC estimator (called normalized curvature) using a general class of kernels, and derives the optimal bandwidth that minimizes the asymptotic mean squared error of the estimator of normalized curvature. It is shown that the optimal bandwidth for the kernel-smoothed normalized curvature estimator should diverge at a slower rate than that of the HAC estimator using the same kernel. An implementation method of the optimal bandwidth for the HAC estimator, which is analogous to the one for probability density estimation by Sheather and Jones (1991), is also developed. The finite sample performance of the new bandwidth choice rule is assessed through Monte Carlo simulations.

Type
Research Article
Information
Econometric Theory , Volume 26 , Issue 3 , June 2010 , pp. 710 - 743
Copyright
Copyright © Cambridge University Press 2009

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Footnotes

I would like to thank Bruce Hansen and Kenneth West for providing advice and encouragement. Comments from two anonymous referees, Gordon Fisher, Nikolay Gospodinov, Yuichi Kitamura (the co-editor), and Victoria Zinde-Walsh substantially helped the revision of this paper. I also thank David Brown, Xiaohong Chen, Sílvia Gonçalves, Guido Kuersteiner, Carlos Martins-Filho, Nour Meddahi, Taisuke Otsu, Katsumi Shimotsu, Gautam Tripathi, and participants at Montreal Econometrics Workshop, 2005 Canadian Economics Association Annual Meetings, and seminars at Concordia University, Hitotsubashi University, Oregon State University, Queen’s University, University of Tokyo, and University of Wisconsin for helpful comments and suggestions.

References

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