02348cas a2200313 a 4500 AALejournalIMF021676 230101c9999 xx r poo 0 0eng d 5.00 USD 9781513574219 2075-8669 BD-DhAAL BD-DhAAL International Monetary Fund. Strategy, Policy, and Review Department. How to Assess Country Risk : The Vulnerability Exercise Approach Using Machine Learning. Washington, D.C. : International Monetary Fund, 2021. 1 online resource (66 pages) Technical Notes and Manuals <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. 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 here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in 'normal times.' The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results. Mode of access: Internet ML Technique imf National Government Expenditures and Related Policies imf Risk Assessment, Supervised Machine Learning and Prediction imf Sudden Stop, Exchange Market Pressure and Fiscal Crisis imf Tax Evasion and Avoidance imf Ve Modeling Toolkit imf Technical Notes and Manuals; Technical Notes and Manuals ; No. 2021/003 Full text available on IMF http://elibrary.imf.org/view/journals/005/2021/003/005.2021.issue-003-en.xml IMF e-Library