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
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
New Economics Papers: this item is included in nep-for, nep-mac and nep-sog
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Journal Article: Yield Curve and Recession Forecasting in a Machine Learning Framework (2015)
Working Paper: Yield curve and Recession Forecasting in a Machine Learning Framework (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:duthrp:2014_008
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