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Estimation and Inference With Weak, Semi‐Strong, and Strong Identification
Author(s) -
Andrews Donald W. K.,
Cheng Xu
Publication year - 2012
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta9456
Subject(s) - mathematics , estimator , quantile , extremum estimator , consistency (knowledge bases) , asymptotic distribution , strong consistency , empirical likelihood , parametric statistics , m estimator , estimation theory , series (stratigraphy) , range (aeronautics) , parameter space , least squares function approximation , statistics , paleontology , materials science , geometry , composite material , biology
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.