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Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Author:
Karim Barhoumi
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,
Seung Mo Choi
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,
Tara Iyer null

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,
Jiakun Li
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,
Franck Ouattara
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,
Mr. Andrew J Tiffin
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Jiaxiong Yao
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Series:
IMF Working Papers
Volume/Issue:
Volume 2022: Issue 088
Language:
English
Publication Date:
06 May 2022
DOI:
https://doi.org/10.5089/9798400210136.001
ISBN:
9798400210136
ISSN:
1018-5941
Pages:
23
Keywords:
Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19; machine learning approach; data sparsity; GDP statistics; crisis in Sub-Saharan Africa; learning framework; Oil prices; Real effective exchange rates; Africa; Global
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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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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund