Symmetrically Trimmed Least Squares Estimation for Tobit Models
提出替代最大似然估计的对称截尾最小二乘估计法，适用于删失和截断回归模型（Tobit模型），在误差分布宽泛和异方差未知时仍保持一致性和渐近正态性，并给出模拟结果。
This papjer proposes alternatives to maximum likelihood estimation of the censored and truncated regression models (known to economists as "Tobit" models) .The proposed estimators are based on symmetric censoring or truncation of the upper tail of the distribution of the dependent variable.Unlike methods based on the assumption of identically distributed Gaussian errors/ the estimators are consistent and asymptotically normal for a wide class of error distributions and for heteroscedasticity of unknown form.The paper gives the regularity conditions and proofs of these large sample results, demonstrates how to construct consistent estimators of the asymptotic covariance matrices, and presents the results of a simulation study for the censored case.Extensions and limitations of the approach are also considered.