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Could Diffusion Indexes Have Forecasted the Great Depression?

Author

Listed:
  • Gabriel Mathy

    (Department of Economics, American University)

  • Yongchen Zhao

    (Department of Economics, Towson University)

Abstract

Was the Depression forecastable? In this paper, we test how effective diffusion indexes are in forecasting the deepest recession in U.S. history: the Great Depression. Moore (1961) considered the effectiveness of diffusion indexes historically, including for the Great Depression, though he only did so retrospectively and did not forecast out-of-sample. We reconstruct Moore's diffusion indexes for this historical period and make our own comparable indexes for out-of-sample predictions. We find that diffusion indexes, including the horizon-specific ones we produce, can nowcast turning points fairly well. Forecasting remains difficult, but our results suggest that the initial downturn in 1929 may be forecastable months before the Great Crash. This is a novel result, as previous authors had generally found the Depression was not forecastable.

Suggested Citation

  • 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.
    • Handle: RePEc:tow:wpaper:2023-05
    as

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    File URL: http://webapps.towson.edu/cbe/economics/workingpapers/2023-05.pdf
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    References listed on IDEAS

    as
    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Mathy, Gabriel & Stekler, Herman, 2017. "Expectations and forecasting during the Great Depression: Real-time evidence from the business press," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 1-15.
    3. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
    4. Dominguez, Kathryn M & Fair, Ray C & Shapiro, Matthew D, 1988. "Forecasting the Depression: Harvard versus Yale," American Economic Review, American Economic Association, vol. 78(4), pages 595-612, September.
    5. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    6. Yongchen Zhao, 2020. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Diffusion Index; Great Depression; Forecasting.;
    All these keywords.

    JEL classification:

    • N12 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - U.S.; Canada: 1913-
    • 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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