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Yield curve and Recession Forecasting in a Machine Learning Framework


  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, Greece)

  • Periklis Gogas

    () (Department of Economics, Democritus University of Thrace, Greece; The Rimini Centre for Economic Analysis, Italy)

  • Maria Matthaiou

    (Department of Economics, Democritus University of Thrace, Greece)

  • Efthymia Chrysanthidou

    (Department of Economics, Democritus University of Thrace, Greece)


In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning (ML) framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to 2011:Q4 in conjunction with the real GDP for the same period, to create a model that can successfully forecast output fluctuations (inflation and output gaps) around its long-run trend. We focus our attention in correctly forecasting the instances of output gaps referred for the purposes of our analysis here as recessions. In this effort, we applied a Support Vector Machines (SVM) technique for classification. The results show that we can achieve an overall forecasting accuracy of 66,7% and a 100% accuracy in forecasting recessions.

Suggested Citation

  • Theophilos Papadimitriou & Periklis Gogas & Maria Matthaiou & Efthymia Chrysanthidou, 2014. "Yield curve and Recession Forecasting in a Machine Learning Framework," Working Paper series 32_14, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:32_14

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    References listed on IDEAS

    1. Menzie Chinn & Kavan Kucko, 2015. "The Predictive Power of the Yield Curve Across Countries and Time," International Finance, Wiley Blackwell, vol. 18(2), pages 129-156, June.
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    7. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    8. Arturo Estrella, 2005. "Why Does the Yield Curve Predict Output and Inflation?," Economic Journal, Royal Economic Society, vol. 115(505), pages 722-744, July.
    9. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
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    14. Dionisios Chionis & Periklis Gogas & Ioannis Pragidis, 2010. "Predicting European Union Recessions in the Euro Era: The Yield Curve as a Forecasting Tool of Economic Activity," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 16(1), pages 1-10, February.
    15. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
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    17. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
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    Cited by:

    1. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    2. N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.

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    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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