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

Author

Listed:
  • Liang Jiang

    (Fanhai International School of Finance, Fudan University)

  • Xiaobin Liu

    (Lingnan College, Sun Yat-sen University)

  • Peter C. B. Phillips

    (Yale University, University of Auckland, and Singapore Management University)

  • Yichong Zhang

    (Singapore Management University)

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.

Suggested Citation

  • Liang Jiang & Xiaobin Liu & Peter C. B. Phillips & Yichong Zhang, 2024. "Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 542-556, March.
    • Handle: RePEc:tpr:restat:v:106:y:2024:i:2:p:542-556
    DOI: 10.1162/rest_a_01089
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