The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit
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 fore...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/14/3085 |
| _version_ | 1797588403041075200 |
|---|---|
| author | Cheng-Hong Yang Tshimologo Molefyane Yu-Da Lin |
| author_facet | Cheng-Hong Yang Tshimologo Molefyane Yu-Da Lin |
| author_sort | Cheng-Hong Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:51:29Z |
| format | Article |
| id | doaj.art-1cffee8da1e74343b94dcf0dc1edfe17 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:51:29Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-1cffee8da1e74343b94dcf0dc1edfe172023-11-18T20:20:19ZengMDPI AGMathematics2227-73902023-07-011114308510.3390/math11143085The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent UnitCheng-Hong Yang0Tshimologo Molefyane1Yu-Da Lin2Department of Information Management, Tainan University of Technology, Tainan 710302, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, TaiwanDepartment of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong 880011, TaiwanEconomic 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.https://www.mdpi.com/2227-7390/11/14/3085machine learningeconomic forecastinggated recurrent unitneural network |
| spellingShingle | Cheng-Hong Yang Tshimologo Molefyane Yu-Da Lin The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit Mathematics machine learning economic forecasting gated recurrent unit neural network |
| title | The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit |
| title_full | The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit |
| title_fullStr | The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit |
| title_full_unstemmed | The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit |
| title_short | The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit |
| title_sort | forecasting of a leading country s government expenditure using a recurrent neural network with a gated recurrent unit |
| topic | machine learning economic forecasting gated recurrent unit neural network |
| url | https://www.mdpi.com/2227-7390/11/14/3085 |
| work_keys_str_mv | AT chenghongyang theforecastingofaleadingcountrysgovernmentexpenditureusingarecurrentneuralnetworkwithagatedrecurrentunit AT tshimologomolefyane theforecastingofaleadingcountrysgovernmentexpenditureusingarecurrentneuralnetworkwithagatedrecurrentunit AT yudalin theforecastingofaleadingcountrysgovernmentexpenditureusingarecurrentneuralnetworkwithagatedrecurrentunit AT chenghongyang forecastingofaleadingcountrysgovernmentexpenditureusingarecurrentneuralnetworkwithagatedrecurrentunit AT tshimologomolefyane forecastingofaleadingcountrysgovernmentexpenditureusingarecurrentneuralnetworkwithagatedrecurrentunit AT yudalin forecastingofaleadingcountrysgovernmentexpenditureusingarecurrentneuralnetworkwithagatedrecurrentunit |