Linear Regressions with Combined Data

Xavier D'Haultfoeuille, Christophe Gaillac and Arnaud Maurel

Papers from arXiv.org

Abstract: We study linear regressions 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, with the sharp bounds taking a simple form. We obtain tighter bounds when variables observed in both datasets, but not included in the regression of interest, are available, even if these variables are not subject to specific restrictions. We develop computationally simple and asymptotically normal estimators of the bounds. Finally, we apply our methodology to estimate racial disparities in patent approval rates and to evaluate the effect of patience and risk-taking on educational performance.

Date: 2024-12, Revised 2025-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://arxiv.org/pdf/2412.04816 Latest version (application/pdf)

Related works:
Working Paper: Linear Regressions with Combined Data (2025)
Working Paper: Linear Regressions with Combined Data (2025)
Working Paper: Linear Regressions with Combined Data (2025)
Working Paper: Linear Regressions with Combined Data (2025)
Working Paper: Linear Regressions with Combined Data (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2412.04816

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().