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Africa > Uganda

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

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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
Cover IMF Working Papers
VAR meets DSGE: Uncovering the Monetary Transmission Mechanism in Low-Income Countries
Ms. Grace B Li
,
Mr. Stephen A. O'Connell
,
Mr. Christopher S Adam
,
Mr. Andrew Berg
, and
Mr. Peter J Montiel
VAR methods suggest that the monetary transmission mechanism may be weak and unreliable in low-income countries (LICs). But are structural VARs identified via short-run restrictions capable of detecting a transmission mechanism when one exists, under research conditions typical of these countries? Using small DSGEs as data-generating processes, we assess the impact on VAR-based inference of short data samples, measurement error, high-frequency supply shocks, and other features of the LIC environment. The impact of these features on finite-sample bias appears to be relatively modest when identification is valid—a strong caveat, especially in LICs. However, many of these features undermine the precision of estimated impulse responses to monetary policy shocks, and cumulatively they suggest that “insignificant” results can be expected even when the underlying transmission mechanism is strong.
Keywords:
WP; monetary policy; interest rate; transmission mechanism; Monetary Transmission Mechanism; Low Income Countries; Vector Autoregression Methods; Monte Carlo Methods; monetary policy shock; LIC environment; GDP gap; inflation rate; interest rate target; estimation environment; monetary policy effect; research environment; LIC context; features of the estimation environment; exchange-rate elasticity; monetary policy rule; Vector autoregression; Dynamic stochastic general equilibrium models; Structural vector autoregression; Supply shocks; Africa