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Do leading indicators forecast U.S. recessions? A nonlinear re-evaluation using historical data

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journal contribution
posted on 03.01.2018, 11:30 by Vasilios Plakandaras, Juncal Cunado, Rangan Gupta, Mark Wohar
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.

History

School

  • Business and Economics

Department

  • Business

Published in

International Finance

Volume

20

Issue

3

Pages

289–316

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.

Publisher

© Wiley

Version

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

2017-10-12

Copyright date

2017

ISSN

1367-0271

eISSN

1468-2362

Language

en