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

• Tác giả: Liang Jiang, Xiaobin Liu, Peter C. B Phillips, Yichong Zhang
• Ngôn ngữ: eng
• Ký hiệu phân loại: 001.434 Experimental method
• Thông tin xuất bản: 2020
• Bộ sưu tập: Metadata
• ID: 164528

Tóm tắt

Comment: 94 pagesThis 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.