This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment...

Traditional instrumental variable estimators do not generally estimate effects for the treated population but for the unobserved population of compliers. On the other hand, when there is one-sided non-compliance, they do identify effects for the treated because the populations of treated and...

We introduce a nonparametric estimator for local quantile treatment effects in the regression discontinuity (RD) design. The procedure uses local distribution regression to estimate the marginal distributions of the potential outcomes. We illustrate the procedure through Monte Carlo simulations...

In an evaluation of a job-training program, the influence of the program on the individual earnings capacity is important, because it reflects the program effect on human capital. Estimating these effects is complicated because earnings are observed for employed individuals only, and employment...

In this article, we discuss the implementation of various estimators proposed to estimate quantile treatment effects. We distinguish four cases involv- ing conditional and unconditional quantile treatment effects with either exogenous or endogenous treatment variables. The introduced ivqte...

Counterfactual distributions are important ingredients for policy analysis and de-composition analysis in empirical economics. In this article we develop modelling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider...