Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions

Giuseppe Ragusa ()

No 80906, Working Papers from University of California-Irvine, Department of Economics

Abstract: This paper 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 computationally tractable optimization problem. This problem is similar to the one solved by the Generalized Empirical Likelihood estimators of Newey and Smith (2004), 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, unlike the EL estimator, well behaved when the moment restrictions are misspecified.

Keywords: Minimum divergence; GMM; Generalized empirical likelihood; Higher order efficiency; Misspecified models (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2008-05
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Journal Article: Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:irv:wpaper:080906

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