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You are looking at 1 - 2 of 2 items for :

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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
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

for emerging markets and developing economies, including Turkey ( Solmaz and Sanjani 2015 ), Lebanon ( Tiffin 2016 ), India ( Iyer and Gupta 2019 ), and others ( Marini 2016 ; Narita and Yin 2018 ). This paper contributes to the literature by expanding the application to the data-sparse environment of sub-Saharan Africa. 2 Machine-learning algorithms have often performed relatively well when quickly capturing sharp turning points in GDP growth . Jung and others (2018) test for the robustness of machine-learning forecasts in historical crises and find that

Keywords:
GDP data; Google search volume indices; Brent crude; growth in sub-Saharan Africa; purchasing managers index; COVID-19; Machine learning; Sub-Saharan Africa