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

Herman Stekler and Yongchen Zhao

No 2016-15, Working Papers from Towson University, Department of Economics

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.

Keywords: Forecasting recession; real-time data; probability forecast. (search for similar items in EconPapers)
JEL-codes: C43 C53 C55 E37 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2016-09, Revised 2016-09
New Economics Papers: this item is included in nep-for and nep-mac
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http://webapps.towson.edu/cbe/economics/workingpapers/2016-15.pdf First version, 2016 (application/pdf)

Related works:
Journal Article: Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set (2020)
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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Persistent link: https://EconPapers.repec.org/RePEc:tow:wpaper:2016-15

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