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© 2022 International Monetary Fund WP/22/88
IMF Working Paper
African Department
Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Prepared by Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew Tiffin, and Jiaxiong Yao
Authorized for distribution by Papa N’Diaye
May 2022
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.
ABSTRACT: 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.
| JEL Classification Numbers: | C53, C55, E37, F17, O11 |
| Keywords: | Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19 |
| Author’s E-Mail Address: | kbarhoumi@imf.org; schoi@imf.org; tIyer@imf.org; jli5@imf.org fouattara@imf.org; atiffin@imf.org; jyao@imf.org |
| JEL Classification Numbers: | C53, C55, E37, F17, O11 |
| Keywords: | Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19 |
| Author’s E-Mail Address: | kbarhoumi@imf.org; schoi@imf.org; tIyer@imf.org; jli5@imf.org fouattara@imf.org; atiffin@imf.org; jyao@imf.org |
Title Page
WORKING PAPERS
Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Prepared by Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew Tiffin, and Jiaxiong Yao1
Table of Contents
I. Introduction
II. Related Literature
III. Now casting Framework
A. Stage 1: Selecting Predictors
B. Stage 2: Selecting the Best Model (“Horseracing”)
C. Stage 3: Nowcasting
D. Illustrative Examples
IV. The COVID-19 Crisis in Sub-Saharan Africa
V. Conclusion
VI. References
VII. Appendix: Concepts and Tools in Machine Learning
The Essence of Machine Learning: Overfitting vs. Underfitting
Key Concepts and Algorithms