Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data

Vasilios Plakandaras, Juncal Cunado (), Rangan Gupta and Mark Wohar ()
Additional contact information
Juncal Cunado: Department of Economics, University of Navarra, Spain

No 201685, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper analyzes to what extent a selection of leading indicators are able to forecast U.S. recessions by means of both dynamic probit models and Support Vector Machines (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models foresee U.S. recession periods more closely than SVM models for up to 6 months ahead, while the SVM models are more accurate at longer horizons. Furthermore, SVM models appear to discriminate between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models include oil, stock returns and the term spread as leading indicators.

Keywords: Dynamic Probit Models; Support Vector Machines; U.S. Recessions (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2016-11
New Economics Papers: this item is included in nep-for, nep-his, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:pre:wpaper:201685

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().