Comparative Analysis of Models for Forecasting Sovereign Debt Crises

Student: Mariya Zhdanova

Faculty: Faculty of World Economy and International Affairs

Educational Programme: World Economy (Bachelor)

Year of Graduation: 2024

Recently, there has been a tendency of an increasing of the sovereign debt in all countries of the world. This leads to uncertainty and boosts the risks of sovereign debt crises. The construction of predictive models will help to identify countries at risk of a crisis in a timely manner, which will allow to take preliminary measures of crisis prevention and mitigation. There is a variety of models, which can be used for predicting the crisis. Recently, machine learning models have become widely used in economic research, while traditional econometric models should not also be forgotten. In this paper, the determinants of the sovereign debt crisis are investigated, and a comparative analysis of the forecasting quality of econometric models and machine learning models is carried out.

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