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

Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any - per assumption...

This paper provides a method to construct simultaneous confidence bands for quantile and quantile effect functions for possibly discrete or mixed discrete-continuous random variables. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction...

With the increase in national debts, pay freezes are imposed for several years in the public sector of some countries, at the risk of decreasing the quality of public services. Since public wage setting policies should account for relevant comparisons with the private sector, we provide novel...

The R package Counterfactual implements the estimation and inference methods of Chernozhukov et al. (2013) for counterfactual analysis. The counterfactual distributions con- sidered are the result of changing either the marginal distribution of covariates related to the outcome variable of...

The Regression Discontinuity Design (RDD) has proven to be a compelling and transparent research design to estimate treatment effects. We provide a review of the main assumptions and key challenges faced when adopting an RDD. We cover the most recent developments and advanced methods, and...

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...

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...