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Asymptotic Efficiency of Semiparametric Two-step GMM

Xiaohong Chen (), Jinyong Hahn and Zhipeng Liao ()

No 1880, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: In this note, we characterize the semiparametric efficiency bound for a class of semiparametric models in which the unknown nuisance functions are identified via nonparametric conditional moment restrictions with possibly non-nested or over-lapping conditioning sets, and the finite dimensional parameters are potentially over-identified via unconditional moment restrictions involving the nuisance functions. We discover a surprising result that semiparametric two-step optimally weighted GMM estimators achieve the efficiency bound, where the nuisance functions could be estimated via any consistent nonparametric procedures in the first step. Regardless of whether the efficiency bound has a closed form expression or not, we provide easy-to-compute sieve based optimal weight matrices that lead to asymptotically efficient two-step GMM estimators.

Keywords: Overlapping Information Sets; Semiparametric Efficiency; Two-Step GMM (search for similar items in EconPapers)
JEL-codes: C14 C31 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2012-10
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Published in Review of Economic Studies (July 2014), 81(3): 919-943

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Related works:
Journal Article: Asymptotic Efficiency of Semiparametric Two-step GMM (2014) Downloads
Working Paper: Asymptotic efficiency of semiparametric two-step GMM (2014) Downloads
Working Paper: Asymptotic efficiency of semiparametric two-step GMM (2012) Downloads
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