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Yield spread selection in predicting recession probabilities

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  • Jaehyuk Choi
  • Desheng Ge
  • Kyu Ho Kang
  • Sungbin Sohn

Abstract

The literature on using yield curves to forecast recessions customarily uses 10‐year–3‐month Treasury yield spread without verification on the pair selection. This study investigates whether the predictive ability of spread can be improved by letting a machine learning algorithm identify the best maturity pair and coefficients. Our comprehensive analysis shows that, despite the likelihood gain, the machine learning approach does not significantly improve prediction, owing to the estimation error. This is robust to the forecasting horizon, control variable, sample period, and oversampling of the recession observations. Our finding supports the use of the 10‐year–3‐month spread.

Suggested Citation

  • Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2023. "Yield spread selection in predicting recession probabilities," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1772-1785, November.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:7:p:1772-1785
    DOI: 10.1002/for.2980
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    Cited by:

    1. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.

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