A Comparison of Two-Stage Estimators of Censored Regression Models
通过蒙特卡洛模拟比较了三种估计量在小样本下的表现，发现两阶段估计量虽能控制选择性偏差，但精度低于子样本普通最小二乘法。
This paper presents a Monte Carlo comparison of the small-sample performance of subsample ordinary least squares, the Heckman-Lee two-stage estimator, and the robust estimator of Lee. Each estimator is considered under bivariate normal, t, and chi-square error structures. The estimates indicate that the Heckman-Lee and Lee estimators do not provide an unequivocal mean square error improvement upon subsample ordinary least squares in small samples. While effectively controlling for selectivity bias, the two-stage estimators suffer a substantial loss of small-sample precision relative to subsample ordinary least squares. Copyright 1991 by MIT Press.