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Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data

Author

Listed:
  • Vasilios Plakandaras

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

  • Juncal Cunado

    (Department of Economics, University of Navarra, Spain)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, South Africa)

  • Mark E. Wohar

    (College of Business Administration, University of Nebraska at Omaha USA, and School of Business and Economics, Loughborough University, UK)

Abstract

This paper analyzes to what extent a selection of leading indicators are able to forecast U.S. recessions by means of both dynamic probit models and Support Vector Machines (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models foresee U.S. recession periods more closely than SVM models for up to 6 months ahead, while the SVM models are more accurate at longer horizons. Furthermore, SVM models appear to discriminate between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models include oil, stock returns and the term spread as leading indicators.

Suggested Citation

  • Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201685
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    References listed on IDEAS

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    More about this item

    Keywords

    Dynamic Probit Models; Support Vector Machines; U.S. Recessions;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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