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Optimal Bandwidth Selection For Robust Generalized Method Of Moments Estimation

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  • Wilhelm, Daniel

Abstract

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically optimal bandwidth that minimizes a higher-order approximation to the asymptotic mean-squared error of the estimator of interest. We show that the optimal bandwidth is of the same order as the one minimizing the mean-squared error of the nonparametric plugin estimator, but the constants of proportionality are significantly different. Finally, we develop a data-driven bandwidth selection rule and show, in a simulation experiment, that it may substantially reduce the estimator’s mean-squared error relative to existing bandwidth choices, especially when the number of moment conditions is large.

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  • Wilhelm, Daniel, 2015. "Optimal Bandwidth Selection For Robust Generalized Method Of Moments Estimation," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1054-1077, October.
    • Handle: RePEc:cup:etheor:v:31:y:2015:i:05:p:1054-1077_00
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