Identification-Robust Subvector Inference

Identication-Robust Subvector Inference

This paper introduces identi…cation-robust subvector tests and con…dence sets (CS’s) that have asymptotic size equal to their nominal size and are asymptotically e¢ cient under strong identi…cation. Hence, inference is as good asymptotically as standard methods under standard regularity conditions, but also is identi…cation robust. The results do not require special structure on the models unde...

Gmm Estimation and Uniform Subvector Inference with Possible Identification Failure

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...

SUPPLEMENT TO GMM ESTIMATION AND UNIFORM SUBVECTOR INFERENCE WITH POSSIBLE IDENTIFICATION FAILURE By

Gmm Estimation and Uniform Subvector Inference with Possible Identification Failure By

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...

Identification-robust inference for endogeneity parameters in linear structural models

We provide a generalization of the Anderson-Rubin (AR) procedure for inference on parameters which represent the dependence between possibly endogenous explanatory variables and disturbances in a linear structural equation (endogeneity parameters). We focus on second-order dependence and stress the distinction between regression and covariance endogeneity parameters. Such parameters have intrin...