Vrontos, Ioannis D and Galakis, John and Panopoulou, Ekaterini and Vrontos, Spyridon (2024) Forecasting GDP growth: the economic impact of COVID-19 Pandemic. Journal of Forecasting, 43 (4). pp. 1042-1086. DOI https://doi.org/10.1002/for.3072
Vrontos, Ioannis D and Galakis, John and Panopoulou, Ekaterini and Vrontos, Spyridon (2024) Forecasting GDP growth: the economic impact of COVID-19 Pandemic. Journal of Forecasting, 43 (4). pp. 1042-1086. DOI https://doi.org/10.1002/for.3072
Vrontos, Ioannis D and Galakis, John and Panopoulou, Ekaterini and Vrontos, Spyridon (2024) Forecasting GDP growth: the economic impact of COVID-19 Pandemic. Journal of Forecasting, 43 (4). pp. 1042-1086. DOI https://doi.org/10.1002/for.3072
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.
Item Type: | Article |
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Uncontrolled Keywords: | Bayesian inference; common and specific predictors; linear regression models; model confidence set; model selection; penalized models; quantile regression models; Linear regression models; Quantile regression models; Bayesian Inference; Model selection; Common and specific predictors; Penalized models; Model Confidence Set |
Divisions: | Faculty of Science and Health Faculty of Social Sciences Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of Faculty of Social Sciences > Essex Business School Faculty of Social Sciences > Essex Business School > Essex Finance Centre |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Nov 2023 15:04 |
Last Modified: | 21 Oct 2025 13:47 |
URI: | http://repository.essex.ac.uk/id/eprint/36888 |
Available files
Filename: Journal of Forecasting - 2024 - Vrontos - Forecasting GDP growth The economic impact of COVID‐19 pandemic.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0