Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2020
Abstract
In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate‐adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets.
Keywords
Quantile treatment effect, bootstrap
Discipline
Econometrics
Research Areas
Econometrics
Publication
Quantitative Economics
Volume
11
Issue
3
First Page
957
Last Page
982
ISSN
1759-7323
Identifier
10.3982/QE1323
Publisher
Econometric Society
Citation
ZHANG, Yichong and ZHENG, Xin.
Quantile treatment effects and bootstrap inference under covariate-adaptive randomization. (2020). Quantitative Economics. 11, (3), 957-982.
Available at: https://ink.library.smu.edu.sg/soe_research/2453
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3982/QE1323