OPTIMAL BANDWIDTH SELECTION FOR ROBUST GENERALIZED METHOD OF MOMENTS ESTIMATION
Econometric Theory, 2015, vol. 31, issue 5, 1054-1077
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 estimatorâ€™s mean-squared error relative to existing bandwidth choices, especially when the number of moment conditions is large.
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Working Paper: Optimal bandwidth selection for robust generalized method of moments estimation (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:31:y:2015:i:05:p:1054-1077_00
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