Yield Curve Point Triplets in Recession Forecasting

type="main" xml:lang="en"> Several studies have highlighted the yield curve's ability to forecast economic activity. These studies use the information provided by the slope of the yield curve—i.e., pairs of short- and long-term interest rates. In this paper, we construct three models for forecasting the positive and negative deviations of real US GDP from its long-run trend over the period from 1976Q3 to 2011Q4: one that uses only pairs of interest rates and two that draw on more than two points from the yield curve. We employ two alternative forecasting methodologies: the probit model, which is commonly used in this line of literature, and the support vector machines (SVM) approach from the area of machine learning. Our results show that we can achieve a 100% out-of-sample forecasting accuracy for negative output gaps (recessions) with both methodologies and an overall accuracy (both inflationary and unemployment gaps) of 80% in the case of the best SVM model. The forecasting performance of our model strengthens the existing evidence that the yield curve can be a useful tool for gauging future economic activity.