Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa

Author/Editor:

Karim Barhoumi ; Seung Mo Choi ; Tara Iyer ; Jiakun Li ; Franck Ouattara ; Andrew J Tiffin ; Jiaxiong Yao

Publication Date:

May 6, 2022

Electronic Access:

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Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary:

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.

Series:

Working Paper No. 2022/088

Frequency:

regular

English

Publication Date:

May 6, 2022

ISBN/ISSN:

9798400210136/1018-5941

Stock No:

WPIEA2022088

Pages:

23

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