 Efficient Estimation Using the Characteristic FunctionMarine Carrasco and Rachidi Kotchoni Post-Print from HAL 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 a that needs to be selected. The aim of the present paper is to provide a way to optimally choose a 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 a. We propose to select the regularization parameter by minimizing an estimate of the AMSE. We show that this procedure delivers a consistent estimator of a. 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. Date: 2017 Published in Econometric Theory, 2017, 33 (2), pp.479-526. ⟨10.1017/S0266466616000025⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01386060 DOI: 10.1017/S0266466616000025 Access Statistics for this paper More papers in Post-Print from HAL |
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