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The Review of Economics and Statistics Vol. 106 No. 2 2024

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

Liang Jiang1; Xiaobin Liu2; Peter C. B. Phillips3; Yichong Zhang4

1 Fanhai International School of Finance, Fudan University , · 2 Lingnan College, Sun Yat-sen University · 3 Yale University, University of Auckland, and Singapore Management University · 4 Singapore Management University

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Abstract

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). The standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair, and thus, is conservative. The analytical inference involves estimating multiple functional quantities that requires several tuning parameters. In this paper, we propose two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. In particular, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.

DOI
10.1162/rest_a_01089
Volume
106
Issue
2
Pages
542-556
Language
en
Sources
bibtex:phds-export.bib openalex openalex crossref

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