A1449
Title: Forecasting GDP growth: The economic impact of COVID-19 Pandemic
Authors: Ekaterini Panopoulou - University of Essex (United Kingdom)
Ioannis Vrontos - Athens University of Economics and Business (Greece)
John Galakis - Iniohos Advisory Services (Switzerland)
Spyros Vrontos - University of Essex (United Kingdom) [presenting]
Abstract: The primary goal is to effectively measure the impact of a severe exogenous shock, such as the COVID-19 pandemic, on aggregate economic activity in Greece and five other Euro Area economies, namely Germany, France, Italy, Spain and Belgium. 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. The 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 the six Euro Area 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 the relationship between real GDP growth and several predictor variables.
