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A Two-Stage Plug-In Bandwidth Selection and Its Implementation for Covariance Estimation

Author

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  • Masayuki Hirukawa

    (Department of Economics, Concordia University and CIREQ)

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.

Suggested Citation

  • Masayuki Hirukawa, 2006. "A Two-Stage Plug-In Bandwidth Selection and Its Implementation for Covariance Estimation," CIRJE F-Series CIRJE-F-431, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2006cf431
    as

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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2006/2006cf431.pdf
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    References listed on IDEAS

    as
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    3. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
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    6. Jansson, Michael, 2002. "Consistent Covariance Matrix Estimation For Linear Processes," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1449-1459, December.
    7. Jones, M. C. & Sheather, S. J., 1991. "Using non-stochastic terms to advantage in kernel-based estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 11(6), pages 511-514, June.
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