Download pdf
Using Big Data to Forecast Macroeconomic Effect during the COVID
- 1 Georgetown University
* Author to whom correspondence should be addressed.
Abstract
The appearance of covid-19 has ravaged the global and triggered a economic recession. This essay aims to predict the macroeconomy after the pendamic shock. We start with an analysis of the correlation between covid and economic mobility, and then try to make predictions about GDP, mainly using some machine learning models. Several machine learning models are built to forecast and then we estimate their performances. In detail, first, we will try to predict US GDP using all models.After estimating their results, we are able to choose the best model among all. Then we use this model to forecast Italy’s GDP in order to make sure its ability at a larger scales. To make comparison, we also use traditional VAR model to predict and get its performance. The conclusion of this paper shows that the LSTM model performs the best among all the machine learning models. However, compared with the traditional VAR autoregressive model, there was still a gap of 2.6 times.
Keywords
machine learning, COVID-19, forecast models
[1]. Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020a). The macroeconomics of epidemics. NBER Working Papers 26882. National Bureau of Economic Research, Inc.
[2]. Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020b). The macroeconomics of testing and quarantining. NBER Working Papers 27104. National Bureau of Economic Research, Inc.
[3]. Acemoglu, D., Chernozhukov, V., Werning, I., & Whinston, M. D. (2020). Optimal targeted lockdowns in a multi-group SIR model. NBER Working Papers 27102. National Bureau of Economic Research, Inc.
[4]. Baqaee, D. R., & Farhi, E. (2020). Supply and demand in disaggregated Keynesian economies with an application to the Covid-19 crisis. CEPR Discussion Papers 14743. C.E.P.R. Discussion Papers.
[5]. Baqaee, D., Farhi, E., Mina, M. J., & Stock, J. H. (2020). Reopening scenarios. NBER Working Papers 27244. National Bureau of Economic Research, Inc.
[6]. Favero, C. A., Ichino, A., & Rustichini, A. (2020). Restarting the economy while saving lives under Covid-19. CEPR Discussion Papers 14664. C.E.P.R. Discussion Papers.
[7]. Barro, R. J., Ursua, J. F., & Weng, J. (2020). The coronavirus and the great influenza pandemic: Lessons from the “Spanish flu” for the coronavirus’s potential effects on mortality and economic activity. NBER Working Papers 26866. National Bureau of Economic Research, Inc.
[8]. Barro, R. J. (2020). Non-pharmaceutical interventions and mortality in U.S. cities during the great influenza pandemic, 1918-1919. NBER Working Papers 27049. National Bureau of Economic Research, Inc.
[9]. Velde, F. (2020). What happened to the US economy during the 1918 influenza pandemic? A view through high-frequency data. Federal Reserve Bank of Chicago Working Paper No. 2020-11.
[10]. Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020). Covid-induced economic uncertainty (No. w26983). National Bureau of Economic Research.
[11]. Chetty, R., Friedman, J. N., & Stepner, M. (2024). The economic impacts of COVID-19: Evidence from a new public database built using private sector data. The Quarterly Journal of Economics, 139(2), 829-889.
[12]. Ludvigson, S. C., Ma, S., & Ng, S. (2020). COVID-19 and the macroeconomic effects of costly disasters (No. w26987). National Bureau of Economic Research.
[13]. Primiceri, G. E., & Tambalotti, A. (2020). Macroeconomic forecasting in the time of COVID-19. Manuscript, Northwestern University, 1-23.
Cite this article
Wei,Y. (2024). Using Big Data to Forecast Macroeconomic Effect during the COVID. Journal of Fintech and Business Analysis,1,45-55.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Journal of Fintech and Business Analysis
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).