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Forecasting Revisions to U.S. Jobs Report Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dorfman Jeffrey H. & Li Wenying & Zhang Jingfang, 2025. "Forecasting Revisions to U.S. Jobs Report Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 25(2), pages 825-845.
    • Handle: RePEc:bpj:bejmac:v:25:y:2025:i:2:p:825-845:n:1005
    DOI: 10.1515/bejm-2024-0145
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    Keywords

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    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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