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• W2107772212 abstract "This article studies the minimum divergence (MD) class of estimators for econometric models specified through moment restrictions. We show that MD estimators can be obtained as solutions to a tractable lower dimensional optimization problem. This problem is similar to the one solved by the generalized empirical likelihood estimators of Newey and Smith (2004 Newey , W. K. , Smith , R. J. ( 2004 ). Higher order properties of GMM and Generalized Empirical Likelihood estimators . Econometrica 72 : 219 – 255 .[Crossref], [Web of Science ®] , [Google Scholar]), but it is equivalent to it only for a subclass of divergences. The MD framework provides a coherent testing theory: tests for overidentification and parametric restrictions in this framework can be interpreted as semiparametric versions of Pearson-type goodness of fit tests. The higher order properties of MD estimators are also studied and it is shown that MD estimators that have the same higher order bias as the empirical likelihood (EL) estimator also share the same higher order mean square error and are all higher order efficient. We identify members of the MD class that are not only higher order efficient, but also, unlike the EL estimator, well behaved when the moment restrictions are misspecified." @default.
• W2107772212 created "2016-06-24" @default.
• W2107772212 creator A5080208793 @default.
• W2107772212 date "2011-04-01" @default.
• W2107772212 modified "2023-10-03" @default.
• W2107772212 title "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions" @default.
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• W2107772212 doi "https://doi.org/10.1080/07474938.2011.553541" @default.
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