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Use Machine Learning to Forecast Economic Recession with Covid-19 Evidence
- 1 Univeristy of Illinois at Urbana Champaign
* Author to whom correspondence should be addressed.
Abstract
This article starts by studying the impact of the COVID-19 pandemic on the United States economy and then delves into the relationship between the pandemic and the economic mobility in U.S. In addition, this article also uses various models to predict the US economy, including vector autoregressive model, support vector machines, eXtreme gradient boosting, light gradient-boosting machine and the long short-term memory network, then comparing the prediction results of the above models to select the relatively optimal model. To test the model, the model is also used to predict Italy's economy and then XGBoost is selected. The limitations of the model will be proposed based on the predicted results. Based on the research, XGBoost model can be applied to forecast economic recession. With further study, we believe such forecast and our understanding of economic processes and economic recession can be improved, and the government can adopt proper policies to alleviate the economic recession.
Keywords
COVID-19, economic forecast, machine learning
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Cite this article
Xu,N. (2024). Use Machine Learning to Forecast Economic Recession with Covid-19 Evidence. Journal of Applied Economics and Policy Studies,4,34-43.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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