Overcoming Data Sparsity : A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa /
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning fram...
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| Beste egile batzuk: | , , |
| Formatua: | Aldizkaria |
| Hizkuntza: | English |
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Washington, D.C. :
International Monetary Fund,
2022.
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| Saila: | IMF Working Papers; Working Paper ;
No. 2022/088 |
| Gaiak: | |
| Sarrera elektronikoa: | Full text available on IMF Full text available on IMF |
| Gaia: | The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics. |
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| Alearen deskribapena: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Deskribapen fisikoa: | 1 online resource (23 pages) |
| Formatua: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Sartu: | Electronic access restricted to authorized BRAC University faculty, staff and students |