Forecasting fiscal crises in emerging markets and low-income countries with machine learning models

Raffaele De Marchi () and Alessandro Moro ()
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Raffaele De Marchi: Bank of Italy

No 1405, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: Pre-existing public debt vulnerabilities have been exacerbated by the effects of the pandemic, raising the risk of fiscal crises in emerging markets and low-income countries. This underscores the importance of models designed to capture the main determinants of fiscal distress episodes and forecast sovereign debt crises. In this regard, our paper shows that machine learning techniques outperform standard econometric approaches, such as the probit model. Our analysis also identifies the variables that are the most relevant predictors of fiscal crises and assesses their impact on the probability of a crisis episode. Finally, the forecasts generated by the machine learning algorithms are used to derive aggregate fiscal distress indices that can signal effectively the build-up of debt-related vulnerabilities in emerging and low-income countries.

Keywords: fiscal crises; debt sustainability; emerging and low-income countries; machine learning techniques (search for similar items in EconPapers)
JEL-codes: C18 C52 F34 H63 H68 (search for similar items in EconPapers)
Date: 2023-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pub
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Journal Article: Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1405_23

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