Semiparametric efficiency in nonlinear LATE models

• 成果类型: Article
• 署名作者: Hong, Han; Nekipelov, Denis
• 署名单位: Stanford University; University of California System; University of California Berkeley
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE43
• 发表日期: 2010
• 页码: 279-304
• 关键词: Semiparametric efficiency bound; local treatment effect; FTP; child achievement; unemployment benefits

摘要

In this paper we study semiparametric efficiency for the estimation of a finite-dimensional parameter defined by generalized moment conditions under the local instrumental variable assumptions. These parameters identify treatment effects on the set of compliers under the monotonicity assumption. The distributions of covariates, the treatment dummy, and the binary instrument are not specified in a parametric form, making the model semiparametric. We derive the semiparametric efficiency bounds for both conditional models and unconditional models. We also develop multistep semiparametric efficient estimators that achieve the semiparametric efficiency bound. To illustrate the efficiency gains from using the optimal semiparametric weights, we design a Monte Carlo study. It demonstrates that our semiparametric estimator performs well in nonlinear models.