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Africa > Uganda

You are looking at 1 - 2 of 2 items for :

  • Type: Journal Issue x
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Cover IMF Working Papers
Panel Nowcasting for Countries Whose Quarterly GDPs are Unavailable
Omer Faruk Akbal
,
Mr. Seung M Choi
,
Mr. Futoshi Narita
, and
Jiaxiong Yao
Quarterly GDP statistics facilitate timely economic assessment, but the availability of such data are limited for more than 60 developing economies, including about 20 countries in sub-Saharan Africa as well as more than two-thirds of fragile and conflict-affected states. To address this limited data availablity, this paper proposes a panel approach that utilizes a statistical relationship estimated from countries where data are available, to estimate quarterly GDP statistics for countries that do not publish such statistics by leveraging the indicators readily available for many countries. This framework demonstrates potential, especially when applied for similar country groups, and could provide valuable real-time insights into economic conditions supported by empirical evidence.
Keywords:
Developing economies; fragile and conflict-affected states; GDP; low-income countries; nowcasting; sub-Saharan Africa; GDP statistic; panel Nowcasting; data availability; growth data; annex II; data availablity; Commodity prices; Purchasing power parity; Data collection; Export fluctuations; Global
Cover IMF Working Papers
Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa
Brandon Buell
,
Reda Cherif
,
Carissa Chen
,
Jiawen Tang
, and
Nils Wendt
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