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

Liang Jiang, Xiaobin Liu, Peter Phillips and Yichong Zhang
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Liang Jiang: Fanhai International School of Finance, Fudan University
Xiaobin Liu: Lingnan College, Sun Yat-sen University
Yichong Zhang: Singapore Management University

The Review of Economics and Statistics, 2024, vol. 106, issue 2, 542-556

Abstract: 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.

Date: 2024
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Related works:
Working Paper: Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs (2021)
Working Paper: Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs (2020)
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