Semiparametric Efficiency of GMM under Approximate Constraints
Paul Rochet (IMT)
TL;DR
This paper revisits the efficiency bounds of GMM estimators in econometrics, providing a new proof of optimality and analyzing their robustness under approximate constraints.
Contribution
It offers a novel proof of Chamberlain's GMM optimality and examines conditions for maintaining efficiency with approximate moment constraints.
Findings
Reestablished Chamberlain's GMM efficiency bound
Identified conditions for GMM efficiency with approximate constraints
Enhanced understanding of semiparametric efficiency in econometrics
Abstract
Generalized empirical likelihood and generalized method of moments are well spread methods of resolution of inverse problems in econometrics. Each method defines a specific semiparametric model for which it is possible to calculate efficiency bounds. By this approach, we provide a new proof of Chamberlain's result on optimal GMM. We also discuss conditions under which GMM estimators remain efficient with approximate moment constraints.
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Taxonomy
TopicsStatistical Methods and Inference · Advanced Statistical Methods and Models · Statistical and numerical algorithms