On the choice between sample selection and two-part models
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样本选择模型与两部模型之间的选择
- 研究问题
- 样本选择模型与两部模型的相对表现是否受到模拟设计中的共线性影响？在什么情形下，模型比较与选择性检验才具有解释力？
- 方法与思路
- 作者重新分析既有 Monte Carlo 设计，指出其数据生成过程中的共线性问题，再用修正设计比较两类模型，并以劳动供给数据说明检验可能受到的影响。
- 材料陈述的贡献
- 摘要称修正共线性设计问题后，样本选择模型的差表现消失，两类模型各有适用条件；并称无共线性问题时可用 t 检验区分二者。
- 阅读关注点
- 应核对共线性的具体来源、设计修正是否改变其他比较条件、两模型的估计目标，以及 t 检验的零假设与有限样本表现。
- 阅读边界
- 仅依据 OpenAlex 所列摘要，无法评估设计修正和结果的稳健性；摘要未给出各模型表现良好的具体条件，不能将其解释为通用模型选择规则。
原文摘要
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.