Publication Type
Journal Article
Version
submittedVersion
Publication Date
3-2024
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.
Keywords
Bootstrap inference, matched pairs, quantile treatment effect, randomized control trials
Discipline
Econometrics
Research Areas
Econometrics
Publication
Review of Economics and Statistics
Volume
106
Issue
2
First Page
1
Last Page
15
ISSN
0034-6535
Identifier
10.1162/rest_a_01089
Publisher
Massachusetts Institute of Technology Press (MIT Press): 12 month embargo
Citation
JIANG, Liang; LIU, Xiaobin; Phillips, Peter C B; and ZHANG, Yichong.
Bootstrap inference for quantile treatment effects in randomized experiments with matched pairs. (2024). Review of Economics and Statistics. 106, (2), 1-15.
Available at: https://ink.library.smu.edu.sg/soe_research/2382
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