A Universal Model for Debt Transparency Based on The Forecast of The Ratio of Debt to GDP
Published in Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 2025
Recommended citation: Ela M., Çankal A., Uçan A., Dörterler M., (2025) A Universal Model for Debt Transparency Based on The Forecast of The Ratio of Debt to GDP, Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(4), 1239-1268 https://doi.org/10.30798/makuiibf.1490441
Debt transparency is a critical global issue, especially for countries that face difficulties in reporting public debt reliably. This study develops a universal prediction model for estimating the debt-to-GDP ratio of PIIGS countries using machine learning methods (Support Vector Regression, XGBoost, Random Forest, Ridge and Lasso Regression) on quarterly data from 2000-2021, achieving R² values of up to 99.9% with the Random Forest and XGBoost models.
Recommended citation: Ela M., Çankal A., Uçan A., Dörterler M., (2025) A Universal Model for Debt Transparency Based on The Forecast of The Ratio of Debt to GDP, Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(4), 1239-1268