Economic Growth Modelling in Africa: An Application of Bayesian Model Averaging

<p>The study seeks to model economic growth in Africa using the Bayesian Model Averaging technique. The dataset for the study is a pooled dataset spanning through 2010 to 2021 yearly for seven variables namely: Economic growth (GDP), inflation, unemployment, government consumption, food production,exchange rate and trade openness in 24 Africa economies. The Bayesian Model Averaging technique is adopted having the capacity to extract the posterior inclusion  probability under different model prior associated with g- priors attributed to different Bayesian Model Sampling  Scheme. Findings from the study shows that, in modelling economic growth in Africa, a BMA with Uniform model prior with EBL gprior is most plausible while on the basis of sub-regions, the most plausible BMA model is Uniform model prior with Hyper gprior having accounted for the highest Posterior Model Probability correlation value. Bayesian models with other variants of g-prior should be explored for better detection of the true determinants of economic growth among feasible identified factors under consideration.</p><p>Keywords:- Economic growth, g-prior, Posterior Model Prior, Posterior Inclusion Probability, BMA.</p&gt