ABSTRACT
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%.
Acknowledgments
«This research is co-financed by Greece and the European Union (European Social Fund – ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (ΙΚΥ)”
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Optimal in the sense of the model generalization to unknown data
2 Our implementation of SVM models is based on LIBSVM, Chang and Lin (Citation2011)
3 From Bootstrap AGGregatING.
4 The measure we use is: , since the data are balanced between the two classes (52.6%-47.4%).
5 To address concerns of possible break-points after the 2008 crisis, we used a Chi-squared test of the hypothesis that the model’s accuracy is different in the period 2004:9 to 2007:12 with respect to 2008:1 to 2020:6. The test has a p-value of 0.0646 thus the forecasting performance does not change over time.