Generalized M-Estimation in Censored Regression Model under Endogeneity

Abstract

We propose and study M-estimation to estimate the parameters in the censored regression model in the presence of endogeneity, i.e., the Tobit model. In the course of this study, we follow two-stage procedures: the first stage consists of applying control function procedures to address the issue of endogeneity using instrumental variables, and the second stage applies the M-estimation technique to estimate the unknown parameters involved in the model. The large sample properties of the proposed estimators are derived and analyzed. The finite sample properties of the estimators are studied through Monte Carlo simulation and a real data application related to women's labor force participation.