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Published online by Cambridge University Press:  19 September 2022

Jinyong Hahn
University of California, Los Angeles
Guido Kuersteiner*
University of Maryland
Maurizio Mazzocco
University of California, Los Angeles
Address correspondence to Guido Kuersteiner, Department of Economics, University of Maryland, Tydings Hall 3145, College Park, MD 20742, USA; e-mail:
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Combining cross-sectional and time-series data is a long and well-established practice in empirical economics. We develop a central limit theory that explicitly accounts for possible dependence between the two datasets. We focus on common factors as the mechanism behind this dependence. Using our central limit theorem (CLT), we establish the asymptotic properties of parameter estimates of a general class of models based on a combination of cross-sectional and time-series data, recognizing the interdependence between the two data sources in the presence of aggregate shocks. Despite the complicated nature of the analysis required to formulate the joint CLT, it is straightforward to implement the resulting parameter limiting distributions due to a formal similarity of our approximations with Murphy and Topel’s (1985, Journal of Business and Economic Statistics 3, 370–379) formula.

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© The Author(s), 2022. Published by Cambridge University Press


We thank Peter Phillips and referees in previous rounds for many helpful suggestions.



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