A Monte Carlo comparison of semiparametric Tobit estimators

This paper focuses on a performance comparison of semiparametric Tobit estimators. Firstly, a conditional expectation version of Horowitz's distribution-free least-squares estimator is proposed, together with a short description of the other estimators considered in the later Monte Carlo experiment. Then, a performance comparison of the following selected estimators is made through a Monte Carlo experiment: the standard Tobit maximum-likelihood estimator, the Buckley-James estimator, Horowitz's distribution-free least-squares estimator, a conditional version of Horowitz's estimator and Powell's least absolute deviations estimator. An empirical example of Engel curve estimation with zero expenditures follows.