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.
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Notes
Imposing a weight parameter \(C\) for each erroneously classified data point in the minimization procedure.
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Acknowledgments
“This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: THALES. Investing in knowledge society through the European Social Fund (MIS 380292).”
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Gogas, P., Papadimitriou, T., Matthaiou, M. et al. Yield Curve and Recession Forecasting in a Machine Learning Framework. Comput Econ 45, 635–645 (2015). https://doi.org/10.1007/s10614-014-9432-0
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DOI: https://doi.org/10.1007/s10614-014-9432-0