新冠疫情的影 响：跟踪撒哈拉以南非洲经济活动的即时播报和大数据

IMF Working PaperAfrican DepartmentImpact of COVID-19: Nowcasting and Big Data to Track Economic Act

IMF Working Paper African Department Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa Prepared by Brandon Buell, Carissa Chen, Reda Cherif, Hyeon-Jae Seo, Jiawen Tang, 12 34 56 78 and Nils Wendt Authorized for distribution by Papa M. N'Diaye April 2021 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 pandemic underscores the critical need fbr 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. JELClassification Numbers: C53, C55, E37,F17, Oil Keywords: COVID-19; Econometric modeling; Economic activity; GDP; Google Search Trends; Mobile payments; NO2; Nowcasting; Short-term forecasting Authors' E-Mails: acherif@imf.org jiawen tang@hks.harvard.edu; ： brandon buell@alumni.haivard.edu; hsao@college.harvard.du; cjchen@college.harvard.edu; 】 nils.wendt@tum.de LEstimation Frameworks 1 We are grateful to Karim Barhoumi and Massimiliano Marcellino for their valuable input and suggestions. We extend our thanks to IMF colleagues for their generous support: Diego Cerdeiro for sharing crucial shipping data, Seung Mo Choi, Fuad Hasanov, Xingwei Hu, Vikram Singh, and IMF seminar participants for their thoughtful feedback and discussion. We are also thankful to the South African National Treasury and the Bank of Zambia for their helpful comments. Thank you to Harvard professors Jeffrey Frankel and Dan Levy for their guidance and MPA/ID program students for their insights. We are grateful to Franck Ouattara for his help with data sourcing. This research collaboration was facilitated by the Harvard College Data Analytics Group and we thank Jerry Huang for his coordination support. Jiawen Tang served as the Project Lead for this research collaboration. All errors are our own.