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Cover IMF Working Papers
Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19
Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao

considered as potential predictors. Nontraditional indicators, such as nighttime lights, nitrogen dioxide (NO 2 ) emissions, and Google search volume indices, also can be considered. Finally, a COVID-19 dummy equal to one during the COVID-19 crisis, or estimating the model with a structural break during the crisis, could help capture the potential impact of the pandemic, which would not be captured by other predictors. Useful predictors are not limited to those compiled within the country . Key economic indicators from neighboring economies or major trading partners may

Keywords:
GDP data; Google search volume indices; Brent crude; growth in sub-Saharan Africa; purchasing managers index; COVID-19; Machine learning; Sub-Saharan Africa
Cover IMF Working Papers
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, Mr. Andrew J Tiffin, and Jiaxiong Yao
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