Yield curve and Recession Forecasting in a Machine Learning Framework
Theophilos Papadimitriou (),
Periklis Gogas (),
Maria Matthaiou and
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Maria Matthaiou: Department of Economics, Democritus University of Thrace, Greece
Efthymia Chrysanthidou: Department of Economics, Democritus University of Thrace, Greece
Working Paper series from Rimini Centre for Economic Analysis
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.
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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:rim:rimwps:32_14
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