EXTREMAL QUANTILE TREATMENT EFFECTS

• 成果类型: Article
• 署名作者: Zhang, Yichong
• 署名单位: Singapore Management University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/17-AOS1673
• 发表日期: 2018
• 页码: 3707-3740
• 关键词: efficient semiparametric estimation; propensity score; likelihood-estimation; Missing Data; inference; models; regression; estimators; bootstrap; identification

摘要

This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close to or equal to zero. Such quantile treatment effects are of interest in many applications, such as the effect of maternal smoking on an infant's adverse birth outcomes. When the quantile index is close to zero, the sparsity of data jeopardizes conventional asymptotic theory and bootstrap inference. When the quantile index is zero, there are no existing inference methods directly applicable in the treatment effect context. This paper addresses both of these issues by proposing new inference methods that are shown to be asymptotically valid as well as having adequate finite sample properties.