Publication Type
Working Paper
Version
publishedVersion
Publication Date
12-2018
Abstract
This paper studies the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose three estimation methods: (1) the simple quantile regression (QR), (2) the QR with strata fixed effects, and (3) the inverse propensity score weighted QR. For the three estimators, we derive their asymptotic distributions uniformly over a set of quantile indexes and show that the estimator obtained from inverse propensity score weighted QR weakly dominates the other two in terms of efficiency, for a wide range of randomization schemes. For inference, we show that the weighted bootstrap tends to be conservative for methods (1) and (2) while has asymptotically exact type I error for method (3). We also show that the covariate-adaptive bootstrap inference is valid for all three methods. 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
First Page
1
Last Page
99
Embargo Period
6-9-2019
Citation
ZHENG, Xin and ZHANG, Yichong.
Quantile treatment effects and bootstrap inference under covariate-adaptive randomization. (2018). 1-99.
Available at: https://ink.library.smu.edu.sg/soe_research/2276
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://arxiv.org/abs/1812.10644