Quantile Treatment Effects in the Presence of Covariates

David Powell

The Review of Economics and Statistics, 2020, vol. 102, issue 5, 994-1005

Abstract: This paper proposes a method to estimate unconditional quantile treatment effects (QTEs) given one or more treatment variables, which may be discrete or continuous, even when it is necessary to condition on covariates. The estimator, generalized quantile regression (GQR), is developed in an instrumental variable framework for generality to permit estimation of unconditional QTEs for endogenous policy variables, but it is also applicable in the conditionally exogenous case. The framework includes simultaneous equations models with nonadditive disturbances, which are functions of both unobserved and observed factors. Quantile regression and instrumental variable quantile regression are special cases of GQR and available in this framework.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (49)

Downloads: (external link)
http://www.mitpressjournals.org/doi/pdf/10.1162/rest_a_00858 (application/pdf)
Access to PDF is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tpr:restat:v:102:y:2020:i:5:p:994-1005

Ordering information: This journal article can be ordered from
https://mitpressjour ... rnal/?issn=0034-6535

Access Statistics for this article

The Review of Economics and Statistics is currently edited by Pierre Azoulay, Olivier Coibion, Will Dobbie, Raymond Fisman, Benjamin R. Handel, Brian A. Jacob, Kareen Rozen, Xiaoxia Shi, Tavneet Suri and Yi Xu

More articles in The Review of Economics and Statistics from MIT Press
Bibliographic data for series maintained by The MIT Press ().