The Journal of Business & Economic Statistics serves as an outlet for methodological contributions in statistics and economics, with applications in microeconomics, macroeconomics, business, and finance.
Current co-editors Yingying Fan (University of Southern California), Michal Kolesár (Princeton University), and Dacheng Xiu (The University of Chicago) work with a team of dedicated associate editors to publish articles on new methods for empirical research that demonstrate value in substantive applications.
JBES stands out for its focus on high-quality research, publishing fewer than 10% of more than 1,000 submissions received each year. The journal embraces interdisciplinary research, and articles frequently adapt ideas from machine learning, computer science, and data science to solve problems in analyzing business and economics datasets. It emphasizes clear motivation and rigorous analysis. Robust, substantive, empirical applications demonstrate exactly how these novel methodologies can yield improved or new answers to policy-relevant questions.
Kolesár captures this balance: “What makes JBES unique, relative to other statistics and econometrics journals, is the emphasis on both theoretical rigor and practical relevance. We don’t pursue mathematical rigor for its own sake; conversely, novel methods that appear to work well in a given application must have some theoretical backing. This ensures that the proposed method is reliable enough to work well in other contexts.”
JBES receives nearly 300,000 article downloads per year.
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