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

Included in

Econometrics Commons

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