Forecasting fiscal crises in emerging markets and low-income countries with machine learning models
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- Raffaele Marchi & Alessandro Moro, 2024. "Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models," Open Economies Review, Springer, vol. 35(1), pages 189-213, February.
References listed on IDEAS
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More about this item
Keywords
fiscal crises; debt sustainability; emerging and low-income countries; machine learning techniques;All these keywords.
JEL classification:
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- F34 - International Economics - - International Finance - - - International Lending and Debt Problems
- H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt
- H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2023-04-03 (Big Data)
- NEP-CMP-2023-04-03 (Computational Economics)
- NEP-PUB-2023-04-03 (Public Finance)
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