Publication Type
Journal Article
Version
submittedVersion
Publication Date
1-2018
Abstract
This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close to or equal to zero. Such quantile treatment effects are of interest in many applications, such as the effect of maternal smoking on an infant’s adverse birth outcomes. When the quantile index is close to zero, the sparsity of data jeopardizes conventional asymptotic theory and bootstrap inference. When the quantile index is zero, there are no existing inference methods directly applicable in the treatment effect context. This paper addresses both of these issues by proposing new inference methods that are shown to be asymptotically valid as well as having adequate finite sample properties.
Keywords
Extreme quantile, Intermediate quantile
Discipline
Econometrics
Research Areas
Econometrics
Publication
Annals of Statistics
Volume
46
Issue
6B
First Page
3707
Last Page
3740
ISSN
0090-5364
Identifier
10.1214/17-AOS1673
Publisher
Institute of Mathematical Statistics
Citation
ZHANG, Yichong.
Extremal quantile treatment effects. (2018). Annals of Statistics. 46, (6B), 3707-3740.
Available at: https://ink.library.smu.edu.sg/soe_research/2207
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/10.1214/17-AOS1673