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Do leading indicators forecast U.S. recessions? A nonlinear re-evaluation using historical data
journal contribution
posted on 2018-01-03, 11:30 authored by Vasilios Plakandaras, Juncal Cunado, Rangan Gupta, Mark WoharThis paper analyses to what extent a selection of leading indicators is able to forecast U.S. recessions, by means of both dynamic probit models and Support Vector Machine (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models predict U.S. recession periods more accurately than SVM models up to six months ahead, while the SVM models are more accurate over longer horizons. Furthermore, SVM models appear to distinguish between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models are those that include oil, stock returns and the term spread as leading indicators.
History
School
- Business and Economics
Department
- Business
Published in
International FinanceVolume
20Issue
3Pages
289–316Citation
PLAKANDARAS, V. ... et al, 2017. Do leading indicators forecast U.S. recessions? A nonlinear re-evaluation using historical data. International Finance, 20 (3), pp. 289–316.Publisher
© WileyVersion
- AM (Accepted Manuscript)
Publisher statement
This is the peer reviewed version of the following article: PLAKANDARAS, V. ... et al, 2017. Do leading indicators forecast U.S. recessions? A nonlinear re-evaluation using historical data. International Finance, 20 (3), pp. 289–316., which has been published in final form at https://doi.org/10.1111/infi.12111. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Publication date
2017-10-12Copyright date
2017ISSN
1367-0271eISSN
1468-2362Publisher version
Language
- en