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

23 Pages Posted: 25 May 2022

See all articles by Karim Barhoumi

Karim Barhoumi

International Monetary Fund (IMF)

Seung Mo Choi

International Monetary Fund (IMF)

Tara Iyer

International Monetary Fund (IMF)

Jiakun Li

International Monetary Fund (IMF)

Franck Ouattara

International Monetary Fund (IMF)

Andrew Tiffin

International Monetary Fund (IMF)

Jiaxiong Yao

International Monetary Fund (IMF)

Date Written: May 2022

Abstract

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.

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

JEL Classification: C53, C55, E37, F17, O11, O33, I12, Q41, F31

Suggested Citation

Barhoumi, Karim and Choi, Seung Mo and Iyer, Tara and Li, Jiakun and Ouattara, Franck and Tiffin, Andrew and Yao, Jiaxiong, Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa (May 2022). IMF Working Paper No. 2022/088, Available at SSRN: https://ssrn.com/abstract=4117838

Karim Barhoumi (Contact Author)

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Seung Mo Choi

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Tara Iyer

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Jiakun Li

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Franck Ouattara

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Andrew Tiffin

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Jiaxiong Yao

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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