Economic Recession Forecasts Using Machine Learning Models Based on the Evidence from the COVID-19 Pandemic

Abstract

In the past several years, the global epidemic has deeply affected the world. In this paper, we conduct research to examine the economic costs produced by COVID-19. First, we look for the relationships between the number of new COVID-19 cases and the statistics on economic mobility, and this could help us explain the economic recession during the pandemic. The results show that people tend to stay at home rather than going outside to other places during the pandemic. Additionally, we use the VAR model to forecast the GDP with data from the preceding 40 years, which might be able to help us in the future with risk management. Even though we fail to obtain the accurate estimate of the GDP, it still provides us with a way to conduct advanced planning. The inaccuracy of COVID-19 instances and other social or political issues that were left out could both contribute to the bias.