A New Framework for Estimation of Quantile Treatment Effects
Nonseparable Disturbance in the Presence of Covariates
Download eBook for Free
| Format | File Size | Notes |
|---|---|---|
| PDF file | 0.2 MB | Use Adobe Acrobat Reader version 10 or higher for the best experience. |
This paper introduces a new framework for quantile estimation. Quantile regression techniques have proven to be extremely valuable in understanding the relationship between explanatory variables and the conditional distribution of the outcome variable. Quantile regression allows the effect of the explanatory variables to vary based on a nonseparable disturbance term, frequently interpreted as "unobserved proneness" for the outcome, and provides conditional quantile treatment effects. Researchers are typically interested in the impact of the treatment variables on the unconditional distribution of the outcome. Additional covariates may be necessary (or simply desirable) for identification but adding these variables alters the interpretation of the resulting estimates as some of the "unobserved proneness" becomes observed and the disturbance term is separated. The Generalized Quantile Regression (GQR) estimator provides unconditional quantile treatment effects - the impact of the treatment variables on the unconditional distribution of the outcome variables. The control variables are conditioned on for identification or variance reduction but without altering the interpretation of the estimates. This property parallels mean regression. An IV version (IV-GQR) is also introduced. The estimator is extremely straightforward to implement using standard statistical software. Quantile Regression and Instrumental Variables Quantile Regression are special cases of the introduced estimation technique, but the proposed technique provides additional flexibility in the estimation of quantile treatment effects.
This paper series was made possible by the NIA funded RAND Center for the Study of Aging and the NICHD funded RAND Population Research Center.
This report is part of the RAND Corporation Working paper series. RAND working papers are intended to share researchers' latest findings and to solicit informal peer review. They have been approved for circulation by RAND but may not have been formally edited or peer reviewed.
This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.
The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.