GMM ESTIMATION AND UNIFORM SUBVECTOR INFERENCE WITH POSSIBLE IDENTIFICATION FAILURE

Donald Andrews () and Xu Cheng

Econometric Theory, 2014, vol. 30, issue 2, 287-333

Abstract: This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CSs) 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 CSs are established. The paper also establishes the correct asymptotic sizes of “robust” GMM-based Wald, t, and quasi-likelihood ratio tests and CSs 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.

Date: 2014
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Citations: View citations in EconPapers (21)

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Related works:
Working Paper: GMM Estimation and Uniform Subvector Inference with Possible Identification Failure (2013)
Working Paper: GMM Estimation and Uniform Subvector Inference with Possible Identification Failure (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:30:y:2014:i:02:p:287-333_00

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