Publication Type

Working Paper

Publication Date

6-2016

Abstract

This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close or equal to zero. Such quantile treatment effects are of interest in many economic 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 establishes new estimation and inference theory for cases close or equal to zero. In addition, finite sample properties of the new procedures are illustrated through both simulation studies and empirical applications.

Keywords

Extreme quantile, Intermediate quantile

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

126

City or Country

Singapore

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://econ.arizona.edu/sites/econ/files/zhang_seminar_oct2016.pdf

Included in

Econometrics Commons

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