This article examines the relationship between two alternative approaches, instrumental variables and control function procedures, for estimating the impact of endogenous treatment effects. Although it is well known that the two approaches generate comparable estimates, the relationship between...

This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with...

Sample selection models provide an important way of accounting for economic decisions that combine discrete and continuous choices and of correcting for nonrandom sampling. Nonparametric estimators for these models are developed in this paper. These can be used for estimating shapes and...

We analyze the role of selection bias in generating changes in the observed distribution of female hourly wages in the United States using CPS data for the years 1975 to 2020. We account for selection bias from the employment decision by modeling the distribution of the number of working hours...

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...

An innovation which bypasses the need for instruments when estimating endogenous treatment effects is identification via conditional second moments. The most general of these approaches is Klein and Vella (2010) which models the conditional variances semiparametrically. While this is attractive,...