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How to Assess Country Risk : The Vulnerability Exercise Approach Using Machine Learning.
Author
International Monetary Fund. Strategy, Policy, & Review Department
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Format
Book
Language
English
Published/Created
Washington, D.C. : International Monetary Fund, 2021.
Description
1 online resource (66 pages).
Details
Subject(s)
Country risk
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Machine learning
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Risk assessment
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Series
Technical Notes and Manuals; Technical Notes and Manuals ; No. 2021/003
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Technical Notes and Manuals
Summary note
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.
Bibliographic references
Includes bibliographical references.
Source of description
Description based on publisher supplied metadata and other sources.
Contents
Executive summary
Introduction
Machine learning and crisis forecasting
Models and estimation strategy
Communicating results
External sector model
Fiscal sector model
Financial sector model
Real sector model
Conclusion and next steps
References
Annexes.
Show 9 more Contents items
Other title(s)
How to Assess Country Risk
ISBN
1-5135-7852-9
Doi
10.5089/9781513574219.005
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Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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