Yield curve and Recession Forecasting in a Machine Learning Framework

Theophilos Papadimitriou, Periklis Gogas, Maria Matthaiou and Efthymia Chrysanthidou
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Maria Matthaiou: Department of Economics, Democritus University of Thrace, Greece
Efthymia Chrysanthidou: Department of Economics, Democritus University of Thrace, Greece

Working Paper series from Rimini Centre for Economic Analysis

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 (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.

Date: 2014-11
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Related works:
Journal Article: Yield Curve and Recession Forecasting in a Machine Learning Framework (2015)
Working Paper: Yield Curve and Recession Forecasting in a Machine Learning Framework (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:32_14

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