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220928 ||| eng |
| 020 |
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|a 9781513574219
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| 245 |
0 |
0 |
|a How to Assess Country Risk
|b The Vulnerability Exercise Approach Using Machine Learning
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2021
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| 300 |
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|a 66 pages
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| 651 |
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4 |
|a Greece
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| 653 |
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|a Public finance & taxation
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| 653 |
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|a Financial crises
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| 653 |
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|a Exports and Imports
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| 653 |
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|a National Government Expenditures and Related Policies: General
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| 653 |
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|a Global Financial Crisis, 2008-2009
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| 653 |
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|a Global financial crisis of 2008-2009
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| 653 |
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|a Capital and Ownership Structure
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| 653 |
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|a Goodwill
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Tax administration and procedure
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| 653 |
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|a Early warning systems
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| 653 |
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|a Financial Risk and Risk Management
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| 653 |
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|a Financing Policy
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| 653 |
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|a Financial Risk Management
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| 653 |
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|a Economic & financial crises & disasters
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| 653 |
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|a International Taxation
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| 653 |
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|a Machine learning
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Revenue administration
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| 653 |
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|a Tax Evasion and Avoidance
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| 653 |
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|a Monetary economics
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| 653 |
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|a Value of Firms
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| 653 |
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|a Balance of payments
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| 653 |
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|a Long-term Capital Movements
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| 653 |
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|a Crisis management
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
|
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|a International economics
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| 653 |
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|a Expenditure
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| 653 |
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|a Banks and Banking
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| 653 |
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|a Expenditures, Public
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| 653 |
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|a Banking crises
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| 653 |
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|a Sudden stops
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| 653 |
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|a Capital movements
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| 653 |
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|a Public Finance
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| 653 |
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|a International Investment
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| 653 |
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|a Financial Crises
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| 710 |
2 |
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|a International Monetary Fund
|b Strategy, Policy, & Review Department
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a Technical Notes and Manuals
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| 028 |
5 |
0 |
|a 10.5089/9781513574219.005
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| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/005/2021/003/005.2021.issue-003-en.xml?cid=50276-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a 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
|