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Yield Curve and Recession Forecasting in a Machine Learning Framework

Periklis Gogas (), Theophilos Papadimitriou (), Maria - Artemis Matthaiou () and Efthymia Chrysanthidou ()
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Efthymia Chrysanthidou: Democritus University of Thrace, Department of Economics, Postal: Department of Economics, University Campus, Komotini, 69100, Greece

Authors registered in the RePEc Author Service: Efthymia Chrysanthidou ()

No 8-2014, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics

Abstract: In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning (ML) framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to 2011:Q4 in conjunction with the real GDP for the same period, to create a model that can successfully forecast output fluctuations (inflation and output gaps) around its long-run trend. We focus our attention in correctly forecasting the instances of output gaps referred for the purposes of our analysis here as recessions. In this effort, we applied a Support Vector Machines (SVM) technique for classification. The results show that we can achieve an overall forecasting accuracy of 66,7% and a 100% accuracy in forecasting recessions.

Keywords: Yield Curve; Recession Forecasting; SVM (search for similar items in EconPapers)
JEL-codes: E43 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2014-02-01
New Economics Papers: this item is included in nep-for, nep-mac and nep-sog
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http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2388719 Full text (application/pdf)

Related works:
Journal Article: Yield Curve and Recession Forecasting in a Machine Learning Framework (2015) Downloads
Working Paper: Yield curve and Recession Forecasting in a Machine Learning Framework (2014) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:ris:duthrp:2014_008

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