We propose instrumental variable(IV) estimators for quantile marginal effects and the parameters upon which they depend in a semiparametric outcome model with endogenous discrete treatment variables. We prove identification, consistency, and asymptotic normality of the estimators. We also show...

This paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary. Since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting, we develop estimators for...

This paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary. Since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting, we develop estimators for...

This paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary. Since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting, we develop estimators for...

It is well known that it is important to control the bias in estimating conditional expectations in order to obtain asymptotic normality for quantities of interest (e.g. a finite dimensional parameter vector in semiparametric models or averages of marginal effects in the nonparametric case). For...