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Can machine learning on economic data better forecast the unemployment rate?

Author

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  • Aaron Kreiner
  • John Duca

Abstract

Using FRED data, a machine-learning model outperforms the Survey of Professional Forecasters and other models since 2001 in forecasting the unemployment rate.

Suggested Citation

  • Aaron Kreiner & John Duca, 2020. "Can machine learning on economic data better forecast the unemployment rate?," Applied Economics Letters, Taylor & Francis Journals, vol. 27(17), pages 1434-1437, October.
    • Handle: RePEc:taf:apeclt:v:27:y:2020:i:17:p:1434-1437
    DOI: 10.1080/13504851.2019.1688237
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    Cited by:

    1. Wooi Chen Khoo & Kim Leng Yeah & Shun Yi Hong, 2022. "Modeling unemployment duration, determinants and insurance premium pricing of Malaysia: insights from an upper middle-income developing country," SN Business & Economics, Springer, vol. 2(8), pages 1-25, August.
    2. David Stoneman & John V. Duca, 2024. "Using deep (machine) learning to forecast US inflation in the COVID‐19 era," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 894-902, July.
    3. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
    4. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.

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