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Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set

Author

Listed:
  • Herman Stekler

    (Department of Economics, George Washington University)

  • Yongchen Zhao

    (Department of Economics, Towson University)

Abstract

This paper considers the issue of predicting cyclical turning points using real-time diffusion indexes constructed using a large data set from March 2005 to September 2014. We construct diffusion indexes at the monthly frequency, compare several smoothing and signal extraction methods, and evaluate predictions based on the indexes. Our finding suggest that diffusion indexes are still effective tools in predicting turning points. Using diffusion indexes, along with good judgement, one would have successfully predicted or at least identified the 2008 recession in real time.

Suggested Citation

  • Herman Stekler & Yongchen Zhao, 2016. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Working Papers 2016-15, Towson University, Department of Economics, revised Sep 2016.
    • Handle: RePEc:tow:wpaper:2016-15
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    References listed on IDEAS

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    Cited by:

    1. Gabriel Mathy & Yongchen Zhao, 2023. "Could Diffusion Indexes Have Forecasted the Great Depression?," Working Papers 2023-05, Towson University, Department of Economics, revised Sep 2023.

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    More about this item

    Keywords

    Forecasting recession; real-time data; probability forecast.;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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