Yield curve and Recession Forecasting in a Machine Learning Framework

JEL codes: 
E30, E39
Version Date: 
Nov 2013

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.

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