We introduce a semiparametric estimator for the censored linear regression model. It is based on the regression version of Huber's  M-estimator. It includes Powell's  censored least absolute deviations estimator as a special case and is related to Powell's  symmetrically censored least-squares estimator. We prove strong consistency and derive its asymptotic distribution which is √n-consistent with an easily computable covariance matrix. A small-scale simulation study shows that it works quite well in various cases.
ASJC Scopus subject areas
- Economics and Econometrics
- Social Sciences (miscellaneous)