Forecasting Revisions to U.S. Jobs Report Data

Dorfman Jeffrey H. (), Li Wenying and Zhang Jingfang
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Dorfman Jeffrey H.: Hugh C. Kiger Distinguished Professor, Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC, USA
Li Wenying: Associate Professor of Quantitative Methods, Department of Agricultural Economics and Rural Sociology, Auburn University, Auburn, AL, USA
Zhang Jingfang: Assistant Professor, Alcorn State University, Lorman, MS, USA

The B.E. Journal of Macroeconomics, 2025, vol. 25, issue 2, 825-845

Abstract: In this paper, we demonstrate a forecastable approach to revisions in the BLS’s monthly Employment Situation report using a Bayesian hierarchical model. By incorporating labor market and economic activity measures, our model accurately predicts both the level and sign of data revisions. Enhancing the ability to forecast data revisions can significantly improve financial market efficiency and support better policy decisions by government and central bank officials, who often depend on initial employment estimates or endure time-consuming revisions to achieve a more accurate understanding of the labor market.

Keywords: data revisions; employment; jobs report; hierarchical models (search for similar items in EconPapers)
JEL-codes: C11 E24 E37 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/bejm-2024-0145

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