Author(s)

Yuhuan Huang¹, Erik S. Yan²

¹Department of Finance, Southern University of Science and Technology, Shenzhen, China.
²Carey Business School, Johns Hopkins, Baltimore, USA.

This paper focuses on the use of machine learning models to forecast economic recessions caused by incidents such as the COVID-19 pandemic. Relevant economic variables are selected to fit into the VAR, SVR, Random Forest, and LSTM models. The study examines the cases of the US and Italy, analyzing how the models predict the Euro crisis, 2008 Financial Crisis, and the economic recession induced by COVID-19. Evaluations and comparisons among these models and cases are made to determine appropriate models. Additionally, an analysis based on US 2020 mobility data is applied to demonstrate the difference in economic activities between normal and crisis times.

Economic Recession, COVID-19, Machine Learning Applications, US, Italy

Huang, Y. and Yan, E. (2023) Economic Recession Forecasts Using Machine Learning Models Based on the Evidence from the COVID-19 Pandemic. Modern Economy, 14, 899-922. doi: 10.4236/me.2023.147049.