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

Yongchen Zhao

Journal of Business Cycle Research, 2020, vol. 16, issue 2, No 2, 77-97

Abstract: 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.

Keywords: Business cycle; Diffusion index; Big data; Recession probability; Cyclical downturn (search for similar items in EconPapers)
JEL-codes: C43 C53 C55 E32 E37 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Working Paper: Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set (2016)
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DOI: 10.1007/s41549-020-00046-y

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