Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa

By Brandon Buell, Reda Cherif, Carissa Chen, Hyeon, Jiawen Tang, Nils Wendt

May 1, 2021

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Format: Chicago

Brandon Buell, Reda Cherif, Carissa Chen, Hyeon, Jiawen Tang, and Nils Wendt. Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa, (USA: International Monetary Fund, 2021) accessed September 18, 2024

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 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.

Subject: Econometric analysis, Factor models, Foreign exchange, Machine learning, Mobile banking, Spot exchange rates, Technology, Time series analysis

Keywords: Africa, Data variable, Factor models, GDP YoY, Machine learning, ML model, Mobile banking, Model prediction, Quantile plot, Spot exchange rates, Sub-Saharan Africa, Time series analysis, YoY percent change

Publication Details

Pages:

61
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2021/124
Stock No:

WPIEA2021124
ISBN:

9781513582498
ISSN:

1018-5941

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