Treatment Effects With Censoring and Endogeneity

• 成果类型: Article
• 署名作者: Frandsen, Brigham R.
• 署名单位: Brigham Young University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1017577
• 发表日期: 2015
• 页码: 1745-1752
• 关键词: instrumental variables; quantile regression; models; identification; distributions; inference

摘要

This article develops a nonparametric approach to identification and estimation of treatment effects on censored outcomes when treatment may be endogenous and have arbitrarily heterogenous effects. Identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework. The article proposes a censored quantile treatment effects estimator, derives its asymptotic distribution, and illustrates its performance using Monte Carlo simulations. Even in the exogenous case, the estimator performs better in finite samples than existing censored quantile regression estimators, and performs nearly as well as maximum likelihood estimators in cases where their distributional assumptions hold. An empirical application to a subsidized job training program finds that participation significantly and dramatically reduced the duration of jobless spells, especially at the right tail of the distribution.