Partially Linear Models under Data Combination

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Tác giả: Xavier D'Haultfœuille, Christophe Gaillac, Arnaud Maurel

Ngôn ngữ: eng

Ký hiệu phân loại: 512.5 Linear algebra

Thông tin xuất bản: 2022

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 194896

Comment: The main paper, which stops at page 45, is followed by the online appendix (30 pages) and supplementary material, not for publication (14 pages). Minor changes compared to v2We 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.
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