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

29 Pages Posted: 20 Sep 2016

See all articles by H.O. Stekler

H.O. Stekler

George Washington University - Department of Economics

Yongchen Zhao

Towson University - Department of Economics

Date Written: September 16, 2016

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

JEL Classification: C43, C53, C55, E37

Suggested Citation

Stekler, H.O. and Zhao, Yongchen, Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set (September 16, 2016). Available at SSRN: https://ssrn.com/abstract=2839879 or http://dx.doi.org/10.2139/ssrn.2839879

H.O. Stekler

George Washington University - Department of Economics ( email )

2115 G ST NW
Washington, DC 20052
United States
202-994-1261 (Phone)
202-994-6147 (Fax)

Yongchen Zhao (Contact Author)

Towson University - Department of Economics ( email )

Towson, MD 21204
United States

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