Moderate Deviations of Generalized Method of Moments and Empirical Likelihood Estimators

Taisuke Otsu
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Taisuke Otsu: Cowles Foundation, Yale University, https://cowles.yale.edu/

No 1785, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper studies moderate deviation behaviors of the generalized method of moments and generalized empirical likelihood estimators for generalized estimating equations, where the number of equations can be larger than the number of unknown parameters. We consider two cases for the data generating probability measure: the model assumption and local contaminations or deviations from the model assumption. For both cases, we characterize the first-order terms of the moderate deviation error probabilities of these estimators. Our moderate deviation analysis complements the existing literature of the local asymptotic analysis and misspecification analysis for estimating equations, and is useful to evaluate power and robust properties of statistical tests for estimating equations which typically involve some estimators for nuisance parameters.

Keywords: Generalized method of moments; Empirical likelihood; Moderate deviations; Large deviations (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2011-02
New Economics Papers: this item is included in nep-ecm and nep-mic
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Published in Journal of Multivariate Analysis (2011), 102(8): 1203-1216

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Journal Article: Moderate deviations of generalized method of moments and empirical likelihood estimators (2011)
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