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

Karim Barhoumi (), Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew Tiffin and Jiaxiong Yao

No 2022/088, IMF Working Papers from International Monetary Fund

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 (search for similar items in EconPapers)
Pages: 23
Date: 2022-05-06
New Economics Papers: this item is included in nep-afr, nep-big and nep-cmp
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