论文标题
数据组合下的部分线性模型
Partially Linear Models under Data Combination
论文作者
论文摘要
当在两个无法链接的不同数据集中观察到感兴趣的结果和一些协变量时，我们研究了部分线性模型。这种类型的数据组合问题在经验微观经济学中经常出现。使用最佳运输理论中的最新工具，我们得出了对尖锐识别集的建设性表征。然后，我们基于此结果并开发了一种新颖的推理方法，该方法利用已确定集合的特定几何特性。我们的方法在有限样品中表现出良好的表现，同时保持非常易于处理。我们采用我们在1850 - 1930年期间研究代际收入流动性的方法。我们的方法使我们可以放松早期工作中使用的排除限制，同时提供信息丰富的信心区域。
We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. We apply our approach to study intergenerational income mobility over the period 1850-1930 in the United States. Our method allows us to relax the exclusion restrictions used in earlier work, while delivering confidence regions that are informative.
