Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set
Herman Stekler and
Yongchen Zhao ()
No 2016-006, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting
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.
Pages: 30 pages
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://www2.gwu.edu/~forcpgm/2016-006.pdf First version, 2016 (application/pdf)
Working Paper: Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2016-006
Access Statistics for this paper
More papers in Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting Contact information at EDIRC.
Bibliographic data for series maintained by GW Economics Department ().