Estimation and Inference With Weak, Semi-Strong, and Strong Identification


  • Donald W. K. Andrews,

    1. Cowles Foundation, Yale University, 30 Hillhouse Ave., New Haven, CT 06520-8281, U.S.A.;
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  • Xu Cheng

    1. Dept. of Economics, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA 19104, U.S.A.;
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    • Andrews gratefully acknowledges the research support of the National Science Foundation via Grants SES-0751517 and SES-1058376. The authors thank a co-editor, three referees, Xiaohong Chen, Patrik Guggenberger, Sukjin Han, Yuichi Kitamura, Ulrich Müller, Peter Phillips, Eric Renault, Frank Schorfheide, Yixiao Sun, and Ed Vytlacil for helpful comments.


This paper analyzes the properties of standard estimators, tests, and confidence sets (CS's) for parameters that are unidentified or weakly identified in some parts of the parameter space. The paper also introduces methods to make the tests and CS's robust to such identification problems. The results apply to a class of extremum estimators and corresponding tests and CS's that are based on criterion functions that satisfy certain asymptotic stochastic quadratic expansions and that depend on the parameter that determines the strength of identification. This covers a class of models estimated using maximum likelihood (ML), least squares (LS), quantile, generalized method of moments, generalized empirical likelihood, minimum distance, and semi-parametric estimators.

The consistency/lack-of-consistency and asymptotic distributions of the estimators are established under a full range of drifting sequences of true distributions. The asymptotic sizes (in a uniform sense) of standard and identification-robust tests and CS's are established. The results are applied to the ARMA(1, 1) time series model estimated by ML and to the nonlinear regression model estimated by LS. In companion papers, the results are applied to a number of other models.