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

Brandon Buell, Reda Cherif, Carissa Chen, Karl Walentin, Jiawen Tang and Nils Wendt

No 2021/124, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: model prediction; quantile plot; ML model; GDP YoY; data variable; YoY percent change; Factor models; Machine learning; Time series analysis; Spot exchange rates; Mobile banking; Africa; Sub-Saharan Africa (search for similar items in EconPapers)
Pages: 61
Date: 2021-05-01
New Economics Papers: this item is included in nep-afr, nep-big, nep-cmp, nep-ecm, nep-for, nep-mac and nep-pay
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