Modeling the Economic Impact of the COVID-19 Pandemic Using Dynamic Panel Models and Seemingly Unrelated Regressions

Abstract

The importance of assessing and estimating the impact of the COVID-19 pandemic on financial markets and economic activity has attracted the interest of researchers and practitioners in recent years. The proposed study aims to explore the pandemic’s impact on the economic activity of six Euro area economies. A class of dynamic panel data models and their corresponding Seemingly Unrelated Regression (SUR) models are developed and applied to model the economic activity of six Eurozone countries. This class of models allows for common and country-specific covariates to affect the real growth, as well as for cross-sectional dependence in the error processes. Estimation and inference for this class of panel models are based on both Bayesian and classical techniques. Our findings reveal that significant heterogeneity exists among the different economies with respect to the explanatory/predictive factors. The impact of the COVID-19 pandemic varied across the Euro area economies under study. Nonetheless, the outbreak of the COVID-19 pandemic profoundly affected real economic activity across all regions and countries. As an exogenous shock of such magnitude, it caused a sharp increase in overall uncertainty that spread quickly across all sectors of the global economy.