GMM Estimation and Uniform Subvector Inference with Possible Identification Failure
86 Pages Posted: 1 Feb 2013
There are 2 versions of this paper
GMM Estimation and Uniform Subvector Inference with Possible Identification Failure
Date Written: January 31, 2013
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
JEL Classification: C12, C15
Suggested Citation: Suggested Citation