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Economic Recession Forecasts Using Machine Learning Models Based on the Evidence from the COVID-19 Pandemic
Department of Finance, Southern University of Science and Technology, Shenzhen, China
Carey Business School, Johns Hopkins, Baltimore, USA
- 1 Department of Finance, Southern University of Science and Technology, Shenzhen, China
- 2 Carey Business School, Johns Hopkins, Baltimore, USA
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Abstract
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.
KeywordsEconomic RecessionCOVID-19Machine Learning ApplicationsUSItaly
- Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020). Covid-Induced Economic Uncertainty. Technical Report, National Bureau of Economic Research. https://doi.org/10.3386/w26983
- Board of Governors of the Federal Reserve System (2020, June 12). Monetary Policy Report. https://www.federalreserve.gov/monetarypolicy/files/20200612_mprfullreport.pdf
- Board of Governors of the Federal Reserve System (2021, February 19). Monetary Policy Report. https://www.federalreserve.gov/monetarypolicy/files/20210219_mprfullreport.pdf
- Chetty, R., Friedman, J. N., Hendren, N., Stepner, M. et al. (2020). The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data. Technical Report, National Bureau of Economic Research. https://doi.org/10.3386/w27431
- Chu, B., & Qureshi, S. (2022). Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast US GDP Growth. Computational Economics. https://doi.org/10.1007/s10614-022-10312-z
- Connaughton, J. E. (2010). Local Economic Impact of the Great Recession of 2008/2009. Review of Regional Studies, 40, 1-4. https://doi.org/10.52324/001c.8157
- Huang, Z. (2022). Research on Economic Data Analysis and Intelligent Prediction Based on BP Neural Network. In Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers (pp. 878-882). The Association for Computing Machinery. https://doi.org/10.1145/3544109.3544372
- Kuyo, M., Mwalili, S., & Okang’o, E. (2021). Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments. Open Journal of Statistics, 11, 620-632. https://scirp.org/Journal/paperinformation.aspx?paperid=112294 https://doi.org/10.4236/ojs.2021.115037
- Levanon, G. (2011). Forecasting Recession and Slow-Down Probabilities with Markov Switching Probabilities as Right-Hand-Side Variables. Business Economics, 46, 99-110. https://doi.org/10.1057/be.2011.8
- Li, X. H. et al. (2013). Using “Random Forest” for Classification and Regression. Chinese Journal of Applied Entomology, 50, 1190-1197.
- Liu, Y. S., & Tang, A. Y. (2022). Prediction Method of Government Economic Situation Based on Big Data Analysis. Digital Government: Research and Practice, 3, 1-16. https://doi.org/10.1145/3563042
- Ludvigson, S. C., Ma, S., & Ng, S. (2020). COVID-19 and the Macroeconomic Effects of Costly Disasters. Technical Report, National Bureau of Economic Research. https://doi.org/10.3386/w26987