## How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning

_Technical Notes and Manuals, May 7, 2021_

## Source details

**Canonical URL:** [How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning](https://www.imf.org/en/publications/tnm/issues/2021/05/07/how-to-assess-country-risk-50276)

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## Bibliographic details
- Published: May 7, 2021
- Series: Technical Notes and Manuals
- DOI: https://doi.org/10.5089/9781513574219.005

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### Overview
- The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks.
- The VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries.
- The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations.
- The new generation of VE models presented leverages machine-learning algorithms to better capture interactions across the economy and non-linear relationships that are not well measured in "normal times."

### Key features of the machine-learning VE models
- Uses supervised Machine Learning (ML technique, ML tool) as core modeling approach.
- Designed to capture:
  - Interactions between different parts of the economy.
  - Non-linear relationships that conventional models may not capture.
- Model development and selection:
  - Performance of machine-learning-based models is evaluated against more conventional models in a horse-race format.
- Emphasis on direct, transparent methods for communicating model results.

### Findings and assessment approach
- The VE provides near-term macroeconomic risk identification across countries, employing a bottom-up, multi-sectoral methodology.
- Machine-learning models can better capture complex interactions and non-linearities compared to conventional approaches.
- Model evaluation follows a competitive comparison (horse-race) between ML-based models and conventional models.

### Communication and usability
- The paper presents methods focused on directness and transparency in communicating model results to stakeholders.

### Subjects and keywords (as listed)
- Subjects: Balance of payments, Banking crises, Early warning systems, Expenditure, Financial crises, Global financial crisis of 2008-2009, Revenue administration, Sudden stops
- Keywords: Banking crises, crisis risk indices, Debt, Early warning systems, Economic Crisis, Economic Growth, Exchange Market Pressure, Financial Crisis, Fiscal Crisis, Global, Global financial crisis of 2008-2009, IMF's Vulnerability Exercise, ML technique, ML tool, Prediction, Risk Assessment, Sudden Stop, Sudden stops, Supervised Machine Learning, VE modeling toolkit, Vulnerability Exercise approach using machine learning

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_Source: https://www.imf.org/en/publications/tnm/issues/2021/05/07/how-to-assess-country-risk-50276_