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

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