Forecasting GDP growth: The economic impact of COVID‐19 pandemic

Abstract

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, and the United States. The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (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 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 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.

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

“…Several studies analyse the impact of COVID-19 on financial markets (Ashraf 2020;Chowdhury et al 2022;Bille and Caporin 2022;Ben-Ahmed et al 2022;Padungsaksawasdi and Treepongkaruna 2023;Ullah 2023;Yiu and Tsang 2023;Pham and Chu 2024, among several others), European trade flows (Caporale et al 2023), foreign investment flows (Kocak and Baris-Tuzemen 2022), international trade (Kim et al 2023;Tudorache and Nicolescu 2023), shipping trade (Xu et al 2021), the labour market (Crossley et al 2021), and tourism (Scarlett 2021). Other studies examine the impact of the COVID-19 pandemic on economic uncertainty (Altig et al 2020;Ng 2021;Abdelkafi et al 2023) and investigate its economic effects (Erdogan et al 2020;Chudik et al 2021;Milani 2021;Chowdhury et al 2022;Matei 2023;Vrontos et al 2024). More recent studies (e.g., Liu and Chu 2024) investigate the impact of financial technology on economic growth during the pandemic using panel regression models.…”

“…These factors are chosen to reflect the economic consequences of an exogenous shock like the COVID-19 outbreak. Such variables have also been utilized in previous studies, for example, by Chudik et al (2021) and Vrontos et al (2024). The data related to the set of common factors are publicly available and have been obtained from the Federal Reserve Bank of the St. Louis' FRED database.…”

“…In addition, other nine country-specific explanatory variables are taken into account, i.e., real productivity, car registrations, the consumer inflation rate, the producer inflation rate, the construction production index, long interest rates, equity market returns, and the Organization for Economic Cooperation and Development (OECD) leading indicator. Thus, the set of country-specific covariates contains macroeconomic and financial-marketrelated indicators, the majority of which are widely followed by both policymakers and practitioners, and have been used in the existing literature, see, for example, Morley and Wong (2020), Baumeister and Guérin (2021), Chudik et al (2021), Cimadomo et al (2022), Berger et al (2023), and Vrontos et al (2024). The list of predictor variables/factors and their transformations to achieve stationarity are presented in Table 1.…”