Asymptotic Efficiency of Semiparametric Two-step GMM

• 成果类型: Article
• 署名作者: Ackerberg, Daniel; Chen, Xiaohong; Hahn, Jinyong; Liao, Zhipeng
• 署名单位: University of Michigan System; University of Michigan; Yale University; University of California System; University of California Los Angeles
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdu011
• 发表日期: 2014
• 页码: 919-943
• 关键词: sequential moment restrictions; models; estimators; bootstrap; variance

摘要

Many structural economics models are semiparametric ones in which the unknown nuisance functions are identified via non-parametric conditional moment restrictions with possibly non-nested or overlapping conditioning sets, and the finite dimensional parameters of interest are over-identified via unconditional moment restrictions involving the nuisance functions. In this article we characterize the semiparametric efficiency bound for this class of models. We show that semiparametric two-step optimally weighted GMM estimators achieve the efficiency bound, where the nuisance functions could be estimated via any consistent non-parametric methods in the first step. Regardless of whether the efficiency bound has a closed form expression or not, we provide easy-to-compute sieve-based optimal weight matrices that lead to asymptotically efficient two-step GMM estimators.