Moderate deviations of generalized method of moments and empirical likelihood estimators

JOURNAL OF MULTIVARIATE ANALYSIS

卷:102

Article

Otsu, Taisuke^1,2 

[1] Yale Univ, Cowles Fdn, New Haven, CT 06520 USA

[2] Yale Univ, Dept Econ, New Haven, CT 06520 USA

关键词: Estimating equation;    Empirical likelihood;    Moderate deviation;   

DOI  :  10.1016/j.jmva.2011.04.002

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

Free