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...
| Κύριος συγγραφέας: | |
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| Άλλοι συγγραφείς: | , , |
| Μορφή: | Επιστημονικό περιοδικό |
| Γλώσσα: | English |
| Έκδοση: |
Washington, D.C. :
International Monetary Fund,
2022.
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| Σειρά: | IMF Working Papers; Working Paper ;
No. 2022/088 |
| Θέματα: | |
| Διαθέσιμο Online: | Full text available on IMF Full text available on IMF |
| Περίληψη: | 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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| Περιγραφή τεκμηρίου: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Φυσική περιγραφή: | 1 online resource (23 pages) |
| Μορφή: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Πρόσβαση: | Electronic access restricted to authorized BRAC University faculty, staff and students |