The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit

Cheng-Hong Yang, Tshimologo Molefyane and Yu-Da Lin ()
Additional contact information
Cheng-Hong Yang: Department of Information Management, Tainan University of Technology, Tainan 710302, Taiwan
Tshimologo Molefyane: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Yu-Da Lin: Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong 880011, Taiwan

Mathematics, 2023, vol. 11, issue 14, 1-17

Abstract: Economic forecasting is crucial in determining a country’s economic growth or decline. Productivity and the labor force must be increased to achieve economic growth, which leads to the growth of gross domestic product (GDP) and income. Machine learning has been used to provide accurate economic forecasts, which are essential to sound economic policy. This study formulated a gated recurrent unit (GRU) neural network model to predict government expenditure, an essential component of gross domestic product. The GRU model was evaluated against autoregressive integrated moving average, support vector regression, exponential smoothing, extreme gradient boosting, convolutional neural network, and long short-term memory models using World Bank data regarding government expenditure from 1990 to 2020. The mean absolute error, root mean square error, and mean absolute percentage error were used as performance metrics. The GRU model demonstrates superior performance compared to all other models in terms of MAE, RMSE, and MAPE (with an average MAPE of 2.774%) when forecasting government spending using data from the world’s 15 largest economies from 1990 to 2020. The results indicate that the GRU can be used to provide accurate economic forecasts.

Keywords: machine learning; economic forecasting; gated recurrent unit; neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/14/3085/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/14/3085/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:14:p:3085-:d:1192712

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().