Linear Regressions with Combined Data

Xavier D'Haultfoeuille, Christophe Gaillac and Arnaud Maurel

No 24-1602, TSE Working Papers from Toulouse School of Economics (TSE)

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 can-not be matched. Traditional approaches obtain point identification by relying, often implicitly, on exclusion restrictions. We show that without such restric-tions, 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)
Date: 2024-12
New Economics Papers: this item is included in nep-ecm
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Related works:
Working Paper: Linear Regressions with Combined Data (2025)
Working Paper: Linear Regressions with Combined Data (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:130028

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