Efficient Estimation Using the Characteristic Function
AbstractThe method of moments proposed by Carrasco and Florens (2000) permits to fully exploit the information contained in the characteristic function and yields an estimator which is asymptotically as efficient as the maximum likelihood estimator. However, this estimation procedure depends on a regularization or tuning parameter \alpha that needs to be selected. The aim of the present paper is to provide a way to optimally choose \alpha by minimizing the approximate mean square error (AMSE) of the estimator. Following an approach similar to that of Newey and Smith (2004), we derive a higher-order expansion of the estimator from which we characterize the fi nite sample dependence of the AMSE on \alpha . We provide a data-driven procedure for selecting the regularization parameter that relies on parametric bootstrap. We show that this procedure delivers a root T consistent estimator of \alpha. Moreover, the data-driven selection of the regularization parameter preserves the consistency, asymptotic normality and efficiency of the CGMM estimator. Simulation experiments based on a CIR model show the relevance of the proposed approach.
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Date of creation: 30 Sep 2013
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Conditional moment restriction; Continuum of moment conditions; Generalized method of moments; Mean square error; Stochastic expansion; Tikhonov regularization;
Other versions of this item:
- Marine Carrasco & Rachidi Kotchoni, 2013. "Efficient estimation using the Characteristic Function," CIRANO Working Papers 2013s-22, CIRANO.
- C00 - Mathematical and Quantitative Methods - - General - - - General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-10-11 (All new papers)
- NEP-ECM-2013-10-11 (Econometrics)
- NEP-ORE-2013-10-11 (Operations Research)
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