Overcoming Data Sparsity : A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa - Barhoumi, Karim; Iyer, Tara.; Li, Jiakun; Mo Choi, Seung (Washington, D.C. : International Monetary Fund, 2022)
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.
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Keywords: Forecasting and Other Model Applications; Large Data Sets; Macroeconomic Analyses of Economic Development; Prices, Business Fluctuations, and Cycles; Trade