Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs

• 成果类型: Article
• 署名作者: Jiang, Liang; Liu, Xiaobin; Phillips, Peter C. B.; Zhang, Yichong
• 署名单位: Fudan University; Sun Yat Sen University; Yale University; University of Auckland; Singapore Management University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01089
• 发表日期: 2024-03
• 页码: 542-556
• 关键词: efficient semiparametric estimation; IMPACT

摘要

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.