Identification and estimation of a heteroskedastic censored regression model with random coefficient dummy endogenous regressors

Abstract

Censoring and the impact of interventions to differ across treated units are two common phenomena in applied micro-econometrics. This paper studies identification and estimation of a heteroskedastic censored regression model with random coefficient dummy regressors. This approach imposes no parametric distributional assumption on error terms and allows for a purely nonparametric participation decision equation. The resulting estimator is shown to be n-consistent and asymptotically normal. Moreover, our estimation approach can readily extend to a censored regression model with multiple random coefficient dummy endogenous regressors. A Monte Carlo simulation shows that our estimator performs reasonably well for finite samples. An application to evaluate the effect of fertility on female labor supply is provided, and the result indicates that the presence of a third child causes work hours per week to decline by approximately 2.5 h.