Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models

Raffaele Marchi () and Alessandro Moro ()
Additional contact information
Raffaele Marchi: International Relations and Economics Directorate

Open Economies Review, 2024, vol. 35, issue 1, No 7, 189-213

Abstract: Abstract Pre-existing public debt vulnerabilities have been exacerbated by the effects of the pandemic, raising the risk of fiscal crises in emerging and low-income countries. This underscores the importance of models aimed at capturing the main determinants of fiscal distress episodes and forecasting the occurrence of sovereign debt crises. In this regard, our paper shows that ensemble tree methods, in particular random forests, are able to outperform standard econometric approaches, such as the probit model. This over-performance is not limited to short-term forecasting horizons, as documented by the previous literature, but holds also at longer horizons. The analysis also identifies the variables that are the most relevant predictors of fiscal crises at different forecasting horizons and provides an assessment of their impact on the probability of observing a crisis episode. Finally, the forecasts of the best performing machine learning algorithm are used to derive aggregate fiscal distress indexes that are able to 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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11079-023-09722-9 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Forecasting fiscal crises in emerging markets and low-income countries with machine learning models (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:openec:v:35:y:2024:i:1:d:10.1007_s11079-023-09722-9

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11079/PS2

DOI: 10.1007/s11079-023-09722-9

Access Statistics for this article

Open Economies Review is currently edited by G.S. Tavlas

More articles in Open Economies Review from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().