Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa

By Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew J Tiffin, Jiaxiong Yao

May 6, 2022

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Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew J Tiffin, and Jiaxiong Yao. Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa, (USA: International Monetary Fund, 2022) accessed November 21, 2024

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

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.

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

Pages:

23
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/088
Stock No:

WPIEA2022088
ISBN:

9798400210136
ISSN:

1018-5941

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