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- W3142904666 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." @default.
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- W3142904666 date "2014-01-01" @default.
- W3142904666 modified "2023-09-27" @default.
- W3142904666 title "Yield Curve and Recession Forecasting in a Machine Learning Framework" @default.
- W3142904666 hasPublicationYear "2014" @default.
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