On the Asymptotic Efficiency of GMM

Jean-Pierre Florens and Marine Carrasco ()

No 436, Econometric Society 2004 North American Winter Meetings from Econometric Society

Abstract: This paper derives conditions under which the generalized method of moments (GMM) estimator is as efficient as the maximum likelihood estimator (MLE). The data are supposed to be drawn from a parametric family and to be stationary Markov. We study the efficiency of GMM in a general framework where the set of moment conditions may be finite, countable infinite, or a continuum. Our main result is the following. GMM estimator is efficient if and only if the true score belongs to the closure of the linear space spanned by the moment conditions. This result extends former ones in two dimensions: (a) the moments may be correlated, (b) the number of moment restrictions may be infinite. It suggests a way to construct estimators that are as efficient as MLE. In the last part of this paper, we show how to calculate the greatest lower bound of instrumental variable estimators

Keywords: Asymptotic efficiency; GMM; infinity of moment conditions; reproducing kernel Hilbert space; efficiency bound. (search for similar items in EconPapers)
JEL-codes: C2 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2004-08-11
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Working Paper: On the Asymptotic Efficiency of GMM (2003)
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Persistent link: http://EconPapers.repec.org/RePEc:ecm:nawm04:436

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