A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using the EM algorithm adapted from the work of Bartolucci and Scaccia (2004). A Monte Carlo study is conducted to examine the small sample properties of this estimator compared to some common alternatives for the estimation of a censored regression model such as the usual tobit model and the CLAD estimator of Powell (1984). Our partially adaptive estimator performed well. The partially adaptive estimator is applied to the Mroz (1987) data on wife’s hours worked. The empirical evidence supports the partially adaptive estimator over the usual tobit model. Keywords; partially adaptive estimator, censored regression model JEL: C240