The primary motivation of this study comes from the major financial and economic challenges that societies are now facing due to several geopolitical events and episodes, financial crises, or random shocks, such as the COVID-19 pandemic. One of the key questions regarding these events/shocks and the uncertainty they cause in the global and individual national economies is their impact on the real economy, specifically on the Gross Domestic Product (GDP), unemployment, energy prices (oil and/or natural gas) and expectations of economic agents, as reflected in the financial markets. The objectives of the proposed study are to introduce, develop and apply appropriate univariate and multivariate statistical and econometric models that consider the characteristics of the underlying data, and the non-linear relationships of the analyzed variables across different states of the economy. 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 competing models and identifies the relevant predictor variables. 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 analyzed countries, suggesting that the quantile modeling approach improves the ability to adequately explain real GDP series compared with the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables.
Zoom link: https://uoc-gr.zoom.us/j/88659969718?pwd=g6bjYPDCuUQo1bzVxjjbgQL4xFN1f3.1
