Yield Curve and Recession Forecasting in a Machine Learning Framework

Periklis Gogas, Theophilos Papadimitriou, Maria Matthaiou and Efthymia Chrysanthidou

Computational Economics, 2015, vol. 45, issue 4, 635-645

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 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 technique for classification. The results show that we can achieve an overall forecasting accuracy of 66.7 and 100 % accuracy in forecasting recessions. These results are compared to the alternative standard logit and probit model, to provide further evidence about the significance of our original model. Copyright Springer Science+Business Media New York 2015

Keywords: Yield curve; Machine learning; SVM; Forecasting; GDP; Recession (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (17)

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Working Paper: Yield curve and Recession Forecasting in a Machine Learning Framework (2014)
Working Paper: Yield Curve and Recession Forecasting in a Machine Learning Framework (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:45:y:2015:i:4:p:635-645

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DOI: 10.1007/s10614-014-9432-0

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