Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/334626 
Year of Publication: 
2025
Series/Report no.: 
CESifo Working Paper No. 12270
Publisher: 
Munich Society for the Promotion of Economic Research - CESifo GmbH, Munich
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.
Subjects: 
data combination
best linear prediction
partial identification
JEL: 
C14
C21
Document Type: 
Working Paper
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