Forecasting U.S. Recessions with Probit Stepwise Regression Models

John Silvia, Sam Bullard and Huiwen Lai

Business Economics, 2008, vol. 43, issue 1, 7-18

Abstract: Yield spreads have been repeatedly used in the literature as the top candidates in predicting future recessions. In this paper, we show that existing model specifications are good but fall short of the performance of more complete models. Applying a probit stepwise regression procedure to a large number of economic indicators, we find models that dramatically outperform those used in the literature. Due to a time series that only began in 1964Q1 and very few historical recessions, any model specification may capture only a few of the economy's many aspects and thus can potentially be biased. Nevertheless, models with better statistical properties should have a better chance to capture the occurrence of recession. Our chosen models are not immune to statistical limitations but should forecast better than the existing models in the literature.Business Economics (2008) 43, 7–18; doi:10.2145/20080101

Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.palgrave-journals.com/be/journal/v43/n1/pdf/be20081a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/be/journal/v43/n1/full/be20081a.html Link to full text HTML (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:buseco:v:43:y:2008:i:1:p:7-18

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11369

Access Statistics for this article

Business Economics is currently edited by Charles Steindel

More articles in Business Economics from Palgrave Macmillan, National Association for Business Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().