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

Author: Barhoumi Karim, Choi Seung Mo, Iyer Tara, Li Jiakun, Ouattara Franck, Tiffin Andrew J, and Yao Jiaxiong

Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19

Volume/Issue: Volume 2022 Issue 088

Publication date: May 2022

ISBN: 9798400211188

$0.00

Add to Cart by clicking price of the language and format you'd like to purchase

Available Languages and Formats


English

Topics covered in this book

This title contains information about the following subjects. Click on a subject if you would like to see other titles with the same subjects.

Economics- Macroeconomics , Economics / General , Sub-Saharan Africa , Economic Activity , GDP , Machine Learning , Nowcasting , COVID-19 , machine learning approach , data sparsity , GDP statistics , crisis in Sub-Saharan Africa , learning framework , Oil prices , Real effective exchange rates , Africa , Global

Also of interest

Measuring Soft Power: A New Global Index

Private Participation and its Discontents: Insights from Large-Scale Surveys

Public Investment Management Bottlenecks in Low-income Countries

Demand for Ethanol Considering Spatially Differentiated Fuel Retailers

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.