On the choice between sample selection and two-part models
On the choice between sample selection and two-part models
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DOI：
10.1016/0304-4076(94)01720-4
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发表时间：
1996-05-01
影响因子：
6.3
通讯作者：
Yu, ST
中科院分区：
文献类型：
--
作者：
Leung, SF;Yu, ST
本文解决了样本选择模型和两部分模型之间的激烈争论。Hay、Leu和Rohrer（1987）以及Manning、Duan和Rogers（1987）最近的蒙特卡罗研究发现，两部分模型的性能优于样本选择模型，即使后者是真正的模型。我们表明，曼宁，段，罗杰斯的负面结果关于样本选择模型是由一个关键的设计问题。我们证明，他们的数据生成过程中产生严重的共线性问题，对样本选择模型的偏见。一旦设计问题得到纠正。样本选择模型的不良性能消失了。我们的蒙特卡罗结果提供了一个更平衡的观点，这两个模型的相对优点，因为每个模型在不同的条件下表现良好。特别是，样本选择模型易受共线性问题的影响，只要不存在共线性问题，就可以使用t检验来区分两个模型。作为一个例子，我们采用Mroz（1987）的劳动力供给数据来说明他的选择性偏差测试可能受到共线性问题的影响。
This paper resolves the vigorous debates between advocates of the sample selection model and the two-part model. Recent Monte Carlo studies by Hay, Leu, and Rohrer (1987) and Manning, Duan, and Rogers (1987) find that the two-part model performs better than the sample selection model even when the latter is the true model. We show that Manning, Duan, and Rogers' negative results regarding the sample selection model are caused by a critical design problem. We demonstrate that their data generating process produces serious collinearity problems that bias against the sample selection model. Once the design problem is rectified. the poor performance of the sample selection model evaporates. Our Monte Carlo results offer a more balanced view on the relative merits of the two models as each model performs well under different conditions. In particular, the sample selection model is susceptible to collinearity problems and a t-test can be used to distinguish between the two models as long as there are no collinearity problems. As an example, we employ Mroz's (1987) labor supply data to illustrate how his tests for selectivity bias might have been affected by collinearity problems.