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In this paper, we use the Eurozone yield curve in an effort to forecast the deviations of the euro-area output (IPI) from its long-run trend. We use various short- and long-term interest rates spanning the period from 2004:9 to 2020:6 in monthly frequency. The interest rates are fed to three machine learning methodologies: Decision Trees, Random Forests, and Support Vector Machines (SVM). These Machine Learning methodologies are then compared to an Elastic-Net Logistic Regression (Logit) model from the area of Econometrics. According to the results, the optimal SVM model coupled with the RBF kernel outperforms the competition reaching an in-sample accuracy of 85.29% and an out-of-sample accuracy of 94.74%.

Mind the gap: forecasting euro-area output gaps with machine learning