Efficient Semiparametric Estimation of Quantile Treatment EffectsEconometrica, 2007, vol. 75, issue 1, pages 259-276 Abstract: This paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics. Identification is achieved without requiring computation of the conditional quantiles of the potential outcomes. Instead, the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps: nonparametric estimation of the propensity score and computation of the difference between the solutions of two separate minimization problems. Root-N consistency, asymptotic normality, and achievement of the semiparametric efficiency bound are shown for that estimator. A consistent estimation procedure for the variance is also presented. Finally, the method developed here is applied to evaluation of a job training program and to a Monte Carlo exercise. Results from the empirical application indicate that the method works relatively well even for a data set with limited overlap between treated and controls in the support of covariates. The Monte Carlo study shows that, for a relatively small sample size, the method produces estimates with good precision and low bias, especially for middle quantiles. Copyright The Econometric Society 2007. Date: 2007 Downloads: (external link) Related works: Ordering information: This journal article can be ordered from Access Statistics for this article Econometrica is edited by Stephen Morris More articles in Econometrica from Econometric Society |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
Econpapers is hosted by the
Swedish Business School at Örebro University.