Forecasting Public Debt in the Euro Area Using Machine Learning: Decision Tools for Financial Markets

The situation of public finance in the eurozone remains a burning issue for certain Euro area countries. The financial markets, the main lenders of the Member States, are more attentive than ever to any factor which could affect the trajectory of public debt in the long term. The risk of bankruptcy of a Member State and a domino effect for the entire monetary union represents the ultimate risk weighing on the Eurozone. This paper aims to forecast the public debt, with a universal model, on a national level within the Euro area. We use a dataset that includes 566 independent variables (economic, financial, institutional, political and social) for 17 Euro area countries, spanning the period from 2000 to 2022 in annual frequency. The dataset is fed to four machine learning (ML) algorithms: Decision Trees, Random Forests, XGBoost and Support Vector Machines (SVM). We also employ the Elastic-Net Regression algorithm from the area of Econometrics. The best model is an XGBoost with an out-of-sample MAPE of 8.41%. Moreover, it outperforms the projections of European Commission and IMF. According to the VIM from XGBoost, the most influential variables are the past values of public debt, the male population in the ages 50-54, the regulatory quality, the control of corruption, the female employment to population ratio for the ages over 15 and the 10 year bond spread.