GMM Estimation and Uniform Subvector Inference with Possible Identification Failure

No 1828, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CS's) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The asymptotic sizes (in a uniform sense) of standard GMM tests and CS's are established. The paper also establishes the correct asymptotic sizes of "robust" GMM-based Wald, t, and quasi-likelihood ratio tests and CS's whose critical values are designed to yield robustness to identification problems. The results of the paper are applied to a nonlinear regression model with endogeneity and a probit model with endogeneity and possibly weak instrumental variables.

Keywords: Asymptotic size; Confidence set; Generalized method of moments; GMM estimator; Identification; Nonlinear models; Test; Wald test; Weak identification (search for similar items in EconPapers)
JEL-codes: C12 C15 (search for similar items in EconPapers)
Pages: 86 pages
Date: 2011-10, Revised 2013-01
Note: Contains supplement
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Citations:

Published in Econometric Theory (April 2014), 20(2): 287-333

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Related works:
Journal Article: GMM ESTIMATION AND UNIFORM SUBVECTOR INFERENCE WITH POSSIBLE IDENTIFICATION FAILURE (2014)
Working Paper: GMM Estimation and Uniform Subvector Inference with Possible Identification Failure (2011)
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