{
  "title": "How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning",
  "publication": "Technical Notes and Manuals, May 7, 2021",
  "sourceUrl": "https://www.imf.org/en/publications/tnm/issues/2021/05/07/how-to-assess-country-risk-50276",
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  "summary": "The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries.",
  "sections": [
    {
      "heading": "Overview",
      "content": "- The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks.\n- The VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries.\n- The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations.\n- 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.\""
    },
    {
      "heading": "Key features of the machine-learning VE models",
      "content": "- Uses supervised Machine Learning (ML technique, ML tool) as core modeling approach.\n- Designed to capture:\n  - Interactions between different parts of the economy.\n  - Non-linear relationships that conventional models may not capture.\n- Model development and selection:\n  - Performance of machine-learning-based models is evaluated against more conventional models in a horse-race format.\n- Emphasis on direct, transparent methods for communicating model results."
    },
    {
      "heading": "Findings and assessment approach",
      "content": "- The VE provides near-term macroeconomic risk identification across countries, employing a bottom-up, multi-sectoral methodology.\n- Machine-learning models can better capture complex interactions and non-linearities compared to conventional approaches.\n- Model evaluation follows a competitive comparison (horse-race) between ML-based models and conventional models."
    },
    {
      "heading": "Communication and usability",
      "content": "- The paper presents methods focused on directness and transparency in communicating model results to stakeholders."
    },
    {
      "heading": "Subjects and keywords (as listed)",
      "content": "- Subjects: Balance of payments, Banking crises, Early warning systems, Expenditure, Financial crises, Global financial crisis of 2008-2009, Revenue administration, Sudden stops\n- 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\n\n---\n\n Content in this bundle\n\n- Tnmea2021003\n  - Tnmea2021003 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Tnmea2021003 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/tnm/issues/2021/05/07/how-to-assess-country-risk-50276"
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    "Published: May 7, 2021",
    "Series: Technical Notes and Manuals",
    "DOI: https://doi.org/10.5089/9781513574219.005",
    "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.\"",
    "Uses supervised Machine Learning (ML technique, ML tool) as core modeling approach.",
    "Designed to capture:",
    "Model development and selection:",
    "Emphasis on direct, transparent methods for communicating model results.",
    "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.",
    "The paper presents methods focused on directness and transparency in communicating model results to stakeholders.",
    "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",
    "**Tnmea2021003**"
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