02212cas a2200349 a 4500 AALejournalIMF022688 230101c9999 xx r poo 0 0eng d 20.00 USD 9798400210136 1018-5941 BD-DhAAL BD-DhAAL Barhoumi, Karim. 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. Washington, D.C. : International Monetary Fund, 2022. 1 online resource (23 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Forecasting and Other Model Applications imf Large Data Sets imf Macroeconomic Analyses of Economic Development imf Prices, Business Fluctuations, and Cycles imf Trade imf Iyer, Tara. Li, Jiakun. Mo Choi, Seung. IMF Working Papers; Working Paper ; No. 2022/088 Full text available on IMF https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=088 IMF e-Library Full text available on IMF https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=097 IMF e-Library