Yield Curve and Recession Forecasting in a Machine Learning Framework

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

JEL Classification: E43

Suggested Citation: Suggested Citation

Gogas, Periklis and Papadimitriou, Theophilos and Matthaiou, Maria and Chrysanthidou, Efthymia, Yield Curve and Recession Forecasting in a Machine Learning Framework (January 31, 2014). Computational Economics, 2015, 45(4), 635-645., Available at SSRN: https://ssrn.com/abstract=2388719 or http://dx.doi.org/10.2139/ssrn.2388719