TY - BOOK ID - 133899735 TI - Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa AU - Buell, Brandon. AU - Cherif, Reda. AU - Chen, Carissa. AU - Walentin, Karl. AU - Tang, Jiawen. AU - Wendt, Nils. PY - 2021 PB - Washington, D.C. : International Monetary Fund, DB - UniCat KW - Macroeconomics KW - Economics: General KW - International Economics KW - Econometrics KW - Intelligence (AI) & Semantics KW - Foreign Exchange KW - Industries: Financial Services KW - Informal Economy KW - Underground Econom KW - Classification Methods KW - Cluster Analysis KW - Principal Components KW - Factor Models KW - Technological Change: Choices and Consequences KW - Diffusion Processes KW - Time-Series Models KW - Dynamic Quantile Regressions KW - Dynamic Treatment Effect Models KW - State Space Models KW - Monetary Systems KW - Standards KW - Regimes KW - Government and the Monetary System KW - Payment Systems KW - Economic & financial crises & disasters KW - Economics of specific sectors KW - Econometrics & economic statistics KW - Machine learning KW - Currency KW - Foreign exchange KW - Computer applications in industry & technology KW - Factor models KW - Econometric analysis KW - Technology KW - Time series analysis KW - Spot exchange rates KW - Mobile banking KW - Currency crises KW - Informal sector KW - Economics KW - Econometric models KW - Banks and banking, Mobile KW - South Africa UR - https://www.unicat.be/uniCat?func=search&query=sysid:133899735 AB - 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. ER -