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

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  • Yongchen Zhao

    (Towson University)

Abstract

This paper considers the usefulness of diffusion indexes in identifying and predicting business cycle turning points in real time using a large data set from March 2005 to September 2014. We construct a monthly diffusion index, compare several smoothing and signal extraction methods, and evaluate predictions based on our index. We document the performance of diffusion-index-based forecasts and compare it against the performance of dynamic-factor-model-based forecasts. Our findings suggest that diffusion indexes remain relevant and effective in identifying turning points. In addition, we show that a diffusion index could outperform a dynamic factor model in identifying the onset of the 2008 recession in real time.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jbuscr:v:16:y:2020:i:2:d:10.1007_s41549-020-00046-y
    DOI: 10.1007/s41549-020-00046-y
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    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. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    3. Herman O Stekler & Raj M Talwar, 2013. "Forecasting the Downturn of the Great Recession," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 48(2), pages 113-120, April.
    4. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
    5. Theodore Alexandrov & Silvia Bianconcini & Estela Bee Dagum & Peter Maass & Tucker S. McElroy, 2012. "A Review of Some Modern Approaches to the Problem of Trend Extraction," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 593-624, November.
    6. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    7. Fossati Sebastian, 2016. "Dating US business cycles with macro factors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 529-547, December.
    8. Kathryn Lundquist & Herman O Stekler, 2012. "Interpreting the Performance of Business Economists During the Great Recession," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 47(2), pages 148-154, April.
    9. Yongchen Zhao, 2021. "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
    10. Geoffrey H. Moore, 1950. "Statistical Indicators of Cyclical Revivals and Recessions," NBER Books, National Bureau of Economic Research, Inc, number moor50-1.
    11. S. S. Alexander & H. O. Stekler, 1959. "Forecasting Industrial Production--Leading Series versus Autoregression," Journal of Political Economy, University of Chicago Press, vol. 67(4), pages 402-402.
    12. Watson, Mark W. & Stock, James H., 2014. "Estimating turning points using large data sets," Scholarly Articles 33192198, Harvard University Department of Economics.
    13. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    14. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    15. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    16. Gerhard Rünstler, 2016. "On the Design of Data Sets for Forecasting with Dynamic Factor Models," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 629-662, Emerald Group Publishing Limited.
    17. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    18. Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.
    19. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    20. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    21. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
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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

    Business cycle; Diffusion index; Big data; Recession probability; Cyclical downturn;
    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
    • 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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