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Quantile‐Based Test for Heterogeneous Treatment Effects

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  • EunYi Chung
  • Mauricio Olivares

Abstract

We introduce a permutation test for heterogeneous treatment effects based on the quantile process. However, tests based on the quantile process often suffer from estimated nuisance parameters that jeopardize their validity, even in large samples. To overcome this problem, we use Khmaladze's martingale transformation. We show that the permutation test based on the transformed statistic controls size asymptotically. Numerical evidence asserts the good size and power performance of our test procedure compared to other popular quantile‐based tests. We discuss a fast implementation algorithm and illustrate our method using experimental data from a welfare reform.

Suggested Citation

  • EunYi Chung & Mauricio Olivares, 2025. "Quantile‐Based Test for Heterogeneous Treatment Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 3-17, January.
    • Handle: RePEc:wly:japmet:v:40:y:2025:i:1:p:3-17
    DOI: 10.1002/jae.3093
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    References listed on IDEAS

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    6. Peng Ding & Avi Feller & Luke Miratrix, 2016. "Randomization inference for treatment effect variation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 655-671, June.
    7. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2017. "Can Variation in Subgroups' Average Treatment Effects Explain Treatment Effect Heterogeneity? Evidence from a Social Experiment," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 683-697, July.
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    10. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
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