Linear Regressions with Combined Data

Xavier D’Haultfoeuille (), Christophe Gaillac and Arnaud Maurel
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Xavier D’Haultfoeuille: CREST-ENSAE

No 2025-04, Working Papers from Center for Research in Economics and Statistics

Abstract: 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.

Keywords: Best linear prediction; data combination; partial identification; inference. (search for similar items in EconPapers)
Pages: 40 pages
Date: 2025-01-24
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http://crest.science/RePEc/wpstorage/2025-04.pdf CREST working paper version (application/pdf)

Related works:
Working Paper: Linear Regressions with Combined Data (2024)
Working Paper: Linear Regressions with Combined Data (2024)
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