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

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  • Cheng-Hong Yang

    (Department of Information Management, Tainan University of Technology, Tainan 710302, Taiwan
    Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    School of Dentistry, Kaohsiung Medical University, Kaohsiung 80708, 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)

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.

Suggested Citation

  • Cheng-Hong Yang & Tshimologo Molefyane & Yu-Da Lin, 2023. "The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3085-:d:1192712
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    References listed on IDEAS

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    1. Ginn, William & Pourroy, Marc, 2022. "The contribution of food subsidy policy to monetary policy in India," Economic Modelling, Elsevier, vol. 113(C).
    2. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    3. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    4. William Ginn & Marc Pourroy, 2022. "The Contribution of Food Subsidy Policy to Monetary Policy in India," Working Papers hal-02944209, HAL.
    5. Barro, Robert J, 1990. "Government Spending in a Simple Model of Endogenous Growth," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 103-126, October.
    6. Wei, Xiaobo & Mohsin, Muhammad & Zhang, Qiongxin, 2022. "Role of foreign direct investment and economic growth in renewable energy development," Renewable Energy, Elsevier, vol. 192(C), pages 828-837.
    7. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    8. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    9. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    10. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    11. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
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