[Séminaire Caen] Linear Regressions with Combined Data

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.
Joint with Christophe Gaillac, University of Geneva and Arnaud Maurel , Duke University

Conditions : MRSH 027, UFR SEGGAT

Source : OpenAgenda – CREM - Centre de Recherche en Économie et Management

Quand ?

- Le 9 novembre de 13:15 à 14:45

Où ?

Université de Caen Normandie, 14000 Caen

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Ne manquez pas [Séminaire Caen] Linear Regressions with Combined Data, un événement exceptionnel à Caen le 9 novembre 2026. Un moment unique à partager en famille ou entre amis. Adresse : Université de Caen Normandie.