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Inference for Iterated GMM Under Misspecification
Author(s) -
Hansen Bruce E.,
Lee Seojeong
Publication year - 2021
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/ecta16274
Subject(s) - estimator , iterated function , generalized method of moments , inference , mathematics , moment (physics) , econometrics , variance (accounting) , delta method , asymptotic distribution , statistics , computer science , economics , artificial intelligence , mathematical analysis , physics , accounting , classical mechanics
This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild misspecification. Moment misspecification causes bias in conventional GMM variance estimators, which can lead to severely oversized hypothesis tests. We show how to consistently estimate the correct asymptotic variance matrix. Our simulation results show that our methods are properly sized under both correct specification and mild to moderate misspecification. We illustrate the method with an application to the model of Acemoglu, Johnson, Robinson, and Yared (2008).