IDEAS home Printed from https://ideas.repec.org/p/fip/fedhwp/103456.html

The Chicago Fed Labor Market Indicators: Bridging the Gap with Alternative Labor Data

Author

Abstract

We present the Chicago Fed Labor Market Indicators (LMI): a twice-monthly release that includes the job-finding rate, the job-separation rate, and a forecast for the U.S. Bureau of Labor Statistics (BLS) unemployment rate. To overcome limitations in data availability, the LMI uses partial least squares to combine CPS-based finding and separation rates with higher-frequency alternative and traditional labor market data series—such as unemployment insurance claims, Google Trends searches, online job postings, and survey-based indicators. Our resulting flow-consistent unemployment rate (FCR) correlates strongly with the BLS unemployment rate, and can be used to characterize the current “low-hire, low-fire” nature of the U.S. labor market. We use a Bayesian linear regression centered on a no-change prior to translate changes in our FCR into a real-time forecast for the next BLS unemployment rate reading. In backtesting spanning 2018–2026, our unemployment rate forecast improves on both a random-walk benchmark and the Bloomberg consensus forecast, with the largest accuracy gains during the Covid-19 pandemic when the unemployment rate rose rapidly in a way that was well-captured by high-frequency labor market data.

Suggested Citation

  • Scott A. Brave & Ben Henken & Ezra Karger & Aryan Safi, 2026. "The Chicago Fed Labor Market Indicators: Bridging the Gap with Alternative Labor Data," Working Paper Series WP 2026-09, Federal Reserve Bank of Chicago.
    • Handle: RePEc:fip:fedhwp:103456
    DOI: 10.21033/wp-2026-09
    as

    Download full text from publisher

    File URL: https://doi.org/10.21033/wp-2026-09
    Download Restriction: no
    File URL: https://libkey.io/10.21033/wp-2026-09?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    2. Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael, 2022. "The perils of working with big data, and a SMALL checklist you can use to recognize them," Business Horizons, Elsevier, vol. 65(4), pages 481-492.
    3. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    4. Scott A. Brave & Ben Henken & Ezra Karger, 2025. "Blending Traditional and Alternative Labor Market Data with CHURN," Chicago Fed Letter, Federal Reserve Bank of Chicago, vol. 506, June.
    5. Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    6. Robert Shimer, 2005. "The Cyclical Behavior of Equilibrium Unemployment and Vacancies," American Economic Review, American Economic Association, vol. 95(1), pages 25-49, March.
    7. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    8. Robert Shimer, 2012. "Reassessing the Ins and Outs of Unemployment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 127-148, April.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Costa, Eduardo André & Silva, Maria Eduarda & Galvão, Ana Beatriz, 2024. "Real-time nowcasting the monthly unemployment rates with daily Google Trends data," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    2. Nathan Schor & Minchul Shin, 2026. "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics," Working Papers 26-38, Federal Reserve Bank of Philadelphia.
    3. Ahmed Ali & Granberg Mark & Troster Victor & Uddin Gazi Salah, 2022. "Asymmetric dynamics between uncertainty and unemployment flows in the United States," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 155-172, February.
    4. Buss, Ginters, 2015. "Search-and-matching frictions and labor market dynamics in Latvia," Dynare Working Papers 45, CEPREMAP.
    5. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    6. Guo, Hongfei & Marín, Juan Miguel & Veiga, Helena, 2026. "Diagnosing and Stabilizing Dynamic Correlations in Multivariate Stochastic Volatility Models," DES - Working Papers. Statistics and Econometrics. WS 50561, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Daniel Aaronson & Scott A. Brave & Michael Fogarty & Ezra Karger & Spencer D. Krane, 2021. "Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade," Working Paper Series WP-2021-05, Federal Reserve Bank of Chicago, revised 18 Jun 2021.
    8. Minchul Shin & Nathan Schor, 2026. "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics," Papers 2603.07458, arXiv.org.
    9. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    10. Oleg Roshka, 2026. "Global Persistence, Local Residual Structure: Forecasting Heterogeneous Investment Panels," Papers 2604.09821, arXiv.org, revised May 2026.
    11. Kruse, Robinson & Leschinski, Christian & Will, Michael, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," Hannover Economic Papers (HEP) dp-571, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    13. M. Mogliani & Thomas Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    14. Li, Haiqi & Zhang, Ni & Zhou, Jin, 2025. "A new self-normalized forecast comparison test," Economics Letters, Elsevier, vol. 256(C).
    15. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
    16. Pawel M. Krolikowski & Kurt G. Lunsford, 2024. "Advance layoff notices and aggregate job loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 462-480, April.
    17. Coroneo, Laura, 2026. "Forecasting for monetary policy," International Journal of Forecasting, Elsevier, vol. 42(1), pages 22-33.
    18. Ginters Buss, 2017. "Wage Formation, Unemployment and Business Cycle in Latvia," Working Papers 2017/01, Latvijas Banka.
    19. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.
    20. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedhwp:103456. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lauren Wiese (email available below). General contact details of provider: https://edirc.repec.org/data/frbchus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.