The Heckman correction for sample selection and its critique
The Heckman correction for sample selection and its critique
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DOI：
10.1111/1467-6419.00104
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发表时间：
2000-02-01
影响因子：
5.3
通讯作者：
Puhani, PA
中科院分区：
文献类型：
--
作者：
Puhani, PA
本文简要介绍了蒙特卡洛研究的有用性Heckman（1976年，1979年）的两步估计估计选择模型。这样的模型经常出现在实证工作中，特别是在微观计量经济学估计工资方程或消费者expenditure.It表明，探索性的工作，检查共线性问题，强烈建议在决定使用哪个估计。在不存在共线性问题的情况下，全信息极大似然估计优于Heckman的有限信息两步法，尽管后者也给出了合理的结果。然而，如果共线性问题占上风，子样本OLS（或两部分模型）是最强大的简单计算的估计。
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.