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  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

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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.

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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

Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Author: Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao