Most sample selection models assume that the errors are independent of the regressors. Under this assumption, all quantile and mean functions are parallel, which implies that quantile estimators cannot reveal any (per definition non-existing) heterogeneity. However, quantile estimators are...

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...

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...

Identi?cation 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...

Using a simulation design that is based on empirical data, a recent study by Huber, Lechner and Wunsch (2012) finds that distance-weighted radius matching with bias adjustment as proposed in Lechner, Miquel and Wunsch (2011) is competitive among a broad range of propensity score-based estimators...

<span lang="EN-GB">In this paper, we assess the impact of firms introducing part-time work schemes for gradual labour market exit of elderly workers on their employees’ labour market outcomes. The analysis is based on unique linked employer-employee data that combine high-quality survey and administrative data....</span>

This paper proposes bootstrap tests for the validity of instrumental variables (IV) in just identified treatment effect models with endogeneity. We demonstrate that the IV assumptions required for the identification of the local average treatment effect (LATE) allow us to both point identify and...

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse pro¬bability weighting, kernel and other variants of matching, as well as different...

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different...