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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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    Cited by:

    1. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    2. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Highly Stratified Experiments," Papers 2307.15181, arXiv.org, revised Mar 2026.
    3. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    4. Bai, Yuehao & Liu, Jizhou & Shaikh, Azeem M. & Tabord-Meehan, Max, 2024. "Inference in cluster randomized trials with matched pairs," Journal of Econometrics, Elsevier, vol. 245(1).
    5. Yuehao Bai & Xun Huang & Joseph P. Romano & Azeem M. Shaikh & Max Tabord-Meehan, 2025. "A New Design-Based Variance Estimator for Finely Stratified Experiments," Papers 2503.10851, arXiv.org, revised May 2025.
    6. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.
    7. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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