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Yield curve and Recession Forecasting in a Machine Learning Framework

Author

Listed:
  • 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)

Abstract

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

    as
    1. 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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    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.

    More about this item

    JEL classification:

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

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