Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
May 6, 2022
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Format: Chicago
Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Summary
Subject: COVID-19, Economic forecasting, Foreign exchange, Health, Machine learning, Oil prices, Prices, Real effective exchange rates, Technology
Keywords: Africa, COVID-19, Crisis in Sub-Saharan Africa, Data sparsity, Economic Activity, GDP, GDP statistics, Global, Learning framework, Machine Learning, Machine learning approach, Nowcasting, Oil prices, Real effective exchange rates, Sub-Saharan Africa
Publication Details
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Pages:
23
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Volume:
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DOI:
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Issue:
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Series:
Working Paper No. 2022/088
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Stock No:
WPIEA2022088
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ISBN:
9798400210136
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ISSN:
1018-5941
