Inference for Iterated GMM Under Misspecification

• 成果类型: Article
• 署名作者: Hansen, Bruce E.; Lee, Seojeong
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; University of New South Wales Sydney
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA16274
• 发表日期: 2021
• 页码: 1419-1447
• 关键词: large-sample properties; generalized-method; Empirical Likelihood; instrumental variables; ASYMPTOTIC REFINEMENTS; ROBUST BOOTSTRAP; least-squares; moments; models; estimator

摘要

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).