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 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