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Title: Do leading indicators forecast U.S. recessions? A nonlinear re-evaluation using historical data
Authors: Plakandaras, Vasilios
Cunado, Juncal
Gupta, Rangan
Wohar, Mark E.
Keywords: Dynamic probit models
Support vector machines
U.S. recessions
Issue Date: 2017
Publisher: © Wiley
Citation: 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.
Abstract: This 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.
Description: This paper is closed access until 12th October 2019.
Version: Accepted for publication
DOI: 10.1111/infi.12111
URI: https://dspace.lboro.ac.uk/2134/27917
Publisher Link: https://doi.org/10.1111/infi.12111
ISSN: 1367-0271
Appears in Collections:Closed Access (Business)

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