A Monte Carlo comparison of semiparametric Tobit estimators
通过蒙特卡洛实验比较了五种半参数Tobit估计量的表现，包括标准Tobit最大似然估计、Buckley-James估计、Horowitz分布自由最小二乘估计及其条件版本、Powell最小绝对偏差估计，并给出了恩格尔曲线估计的实证例子。
Abstract 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.