We study best linear predictions in a context where the outcome of interest and some of the covariates are observed in two different datasets that cannot be matched. Traditional approaches obtain point identification by relying, often implicitly, on exclusion restrictions. We show that without such restrictions, coefficients of interest can still be partially identified and we derive a constructive characterization of the sharp identified set. We then build on this characterization to develop computationally simple and asymptotically normal estimators of the corresponding bounds. We show that these estimators exhibit good finite sample performances.

翻译：本研究探讨在目标变量与部分协变量分别观测于两个无法匹配的数据集情境下的最优线性预测问题。传统方法通常依赖（往往隐含的）排除约束条件来实现点识别。本文证明，即使没有此类约束条件，目标系数仍可被部分识别，并给出了尖锐识别集的构造性表征。基于此表征，我们进一步开发了计算简便且具有渐近正态性的边界估计量。实验表明这些估计量在有限样本中表现出良好的性能。