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EFFICIENT ESTIMATION USING THE CHARACTERISTIC FUNCTION

Published online by Cambridge University Press:  22 February 2016

Marine Carrasco  and
Rachidi Kotchoni
Marine Carrasco
Affiliation:
Université de Montréal
Rachidi Kotchoni*
Affiliation:
African School of Economics
*
*Address correspondence to Rachidi Kotchoni, African School of Economics. E-mail: rachidi.kotchoni@africanschoolofeconomics.com.
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Abstract

The method of moments procedure proposed by Carrasco and Florens (2000) permits full exploitation of 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 α that needs to be selected. The aim of the present paper is to provide a way to optimally choose α by minimizing the approximate mean square error (AMSE) of the estimator. Following an approach similar to that of Donald and Newey (2001), we derive a higher-order expansion of the estimator from which we characterize the finite sample dependence of the AMSE on α. We propose to select the regularization parameter by minimizing an estimate of the AMSE. We show that this procedure delivers a consistent estimator of α. 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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Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 2 , April 2017 , pp. 479 - 526
Copyright
Copyright © Cambridge University Press 2016 

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