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This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design. The distributional impacts of social programs such as welfare, education, training programs and unemployment insurance are of large interest to economists. QTE are an...
Persistent link: https://www.econbiz.de/10013069679
We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of … endogenous assignment variable (like previous earnings). We provide new results on identification and estimation for these …
Persistent link: https://www.econbiz.de/10013029646
This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design (RDD) and proposes simple estimators. Quantile treatment effects are a very helpful tool to characterize the effects of certain interventions on the outcome distribution. The...
Persistent link: https://www.econbiz.de/10013325034
using matching methods. Because precise estimation of the expected counterfactual is particularly important in regions …
Persistent link: https://www.econbiz.de/10013316785
The Regression Kink (RK) design is an increasingly popular empirical method, with more than 20 studies circulated using RK in the last 5 years since the initial circulation of Card, Lee, Pei and Weber (2012). We document empirically that these estimates, which typically use local linear...
Persistent link: https://www.econbiz.de/10013051441
When a treatment unambiguously defines the treatment and control groups at a given time point, its effects are usually found by comparing the two groups' mean responses. But there are many cases where the treatment timing is chosen, for which the conventional approach fails.This paper sets up an...
Persistent link: https://www.econbiz.de/10013085068
. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental …
Persistent link: https://www.econbiz.de/10013324857
a nonparametric adjustment for covariates, building on ideas from the literature on double robust estimation. It is …
Persistent link: https://www.econbiz.de/10012988566
An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These...
Persistent link: https://www.econbiz.de/10013158662
real effects; and (iii) feasible GLS estimation combined with robust inference can increase power considerably whilst …
Persistent link: https://www.econbiz.de/10013061928