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Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Karim BarhoumiSeung Mo ChoiTara IyerJiakun LiFranck OuattaraAndrew TiffinMr. Andrew J TiffinJiaxiong Yao
出版International Monetary Fund, 2022-05-06
主題Business & Economics / Foreign ExchangeBusiness & Economics / Economics / MacroeconomicsBusiness & Economics / Economics / GeneralBusiness & Economics / ForecastingComputers / Artificial Intelligence / GeneralHealth & Fitness / Diseases & Conditions / Contagious (incl. Pandemics)
ISBN9798400210136
URLhttp://books.google.com.hk/books?id=I3lxEAAAQBAJ&hl=&source=gbs_api
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註釋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.