The Heckman Correction for Sample Selection and Its Critique
综述了关于赫克曼两步估计量在样本选择模型中适用性的蒙特卡洛研究，建议在决定估计方法前检查共线性问题，并比较了不同估计量的优劣。
This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman’s (1976, 1979) two‐step estimator for estimating selection models. Such models occur frequently in empirical work, especially in microeconometrics when estimating wage equations or consumer expenditures. It is shown that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full‐information maximum likelihood estimator is preferable to the limited‐information two‐step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two‐Part Model) is the most robust amongst the simple‐to‐calculate estimators.