Forecasting GDP growth: the economic impact of COVID-19 Pandemic

The primary goal of this study is to effectively measure the impact of a severe random shock, such as the COVID-19 pandemic on aggregate economic activity in Greece, seven other Euro Area economies, three Scandinavian countries, as well as U.S.A. The class of linear and quantile predictive regression models is proposed for the analysis of real GDP growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models such as Ridge, Lasso, and Elastic Net are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. Our analysis confirms that the outbreak of the pandemic had a profound effect on the economies under study, and reveals that different predictor variables are able to explain different quantiles of the underlying real GDP growth distribution for analysed countries, suggesting that the quantile modelling approach improves the ability to adequately explain real GDP series compared to the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables.