Advances in Economics, Management and Political Sciences

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Proceedings of the 3rd International Conference on Business and Policy Studies

Series Vol. 80 , 10 May 2024


Open Access | Article

Machine Learning for Economic Forecast Based on Past 30 Years and Covid-19 Pandemic Data

Mo Yu * 1
1 Department of Economics, The Hong Kong University of Science and Technology,

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 80, 129-138
Published 10 May 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Mo Yu. Machine Learning for Economic Forecast Based on Past 30 Years and Covid-19 Pandemic Data. AEMPS (2024) Vol. 80: 129-138. DOI: 10.54254/2754-1169/80/20241897.

Abstract

Along the time, there are many forecasting techniques applied by researchers in various topics in Economics. Traditionally, some of the most popular time-series analysis model like Vector-autoregressive (VAR) are still widely applied in Economics forecasting topics since they can capture sufficient information for a dataset to predict. On the other hands, machining learning and deep-learning algorithm also shades a way for forecasting. In particular, algorithm like Gradient boosting, learning algorithm Long-short-term memory (LSTM) shows their tremendous accuracy when applying to Economic data. Covid-19 leads to one of the largest economic recessions in history in terms of intensity and time. In this article, some of the popular forecasting methods are applied to forecasting GDP data with the record from Covid-19. The empirical analysis is conducted for data in United states to compare the accuracy and divergency for various prediction algorithms. It turns out that machine learning algorithms are well performed in US cases where they generate relatively low forecasting error.

Keywords

Covid-19, Economics Forecasting, Machine learning

References

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2. Chauvet, M., & Potter, S. (2013). Forecasting output. Handbook of economic forecasting, 2, 141-194.

3. Primiceri, G. E., & Tambalotti, A. (2020). Macroeconomic Forecasting in the Time of COVID-19. Manuscript, Northwestern University, 1-23.

4. Hall, A. S. (2018). Machine learning approaches to macroeconomic forecasting. The Federal Reserve Bank of Kansas City Economic Review, 103(63), 2.

5. Yoon, J. (2021). Forecasting of real GDP growth using machine learning models: Gradient boosting and random forest approach. Computational Economics, 57(1), 247-265.

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7. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018, December). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.

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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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Volume Title
Proceedings of the 3rd International Conference on Business and Policy Studies
ISBN (Print)
978-1-83558-419-4
ISBN (Online)
978-1-83558-420-0
Published Date
10 May 2024
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/80/20241897
Copyright
10 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated