Towards Precision Economics: Unveiling GDP Patterns Using Integrated Deep Learning Techniques

Elizabeth Frederick Mumbuli (), Elieneza Nicodemus Abelly (), Melckzedeck Michael Mgimba () and Ayimadu Edwin Twum ()
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Elizabeth Frederick Mumbuli: Dongbei University of Finance and Economics, School of Tourism and Hotel Management
Elieneza Nicodemus Abelly: China University of Geosciences, Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education
Melckzedeck Michael Mgimba: Mbeya University of Science and Technology, Department of Geosciences and Mining Technology
Ayimadu Edwin Twum: Wuhan University, School of Resource and Environmental Science

Computational Economics, 2025, vol. 66, issue 6, No 2, 4513-4541

Abstract: Abstract Economic activities and gross domestic product (GDP) interplay is crucial for fostering sustainable growth and development. Therefore, this study provides enhancements, including the advancement of machine learning, such as the group method of data handling (GMDH) technique to predict gross domestic products in Tanzania. The GMDH model surpassed the particle swarm optimization-artificial neural network (PSO-ANN) and Catboost in both the training and testing phases, with correlation coefficient (R2), the lowest mean absolute error and root mean square error (RMSE).In GMDH training data has R2 = 0.9968, MAE = 0.2478, RMSE = 0.4978, testing dataset with R2 = 0.851, MAE = 0.2267, RMSE = 0.4967. GMDH reduces artificiality by rapidly learning training data and eliminating unnecessary neurons and prediction errors. Hence, Economic activities significantly impact GDP from merchandise trade by about 13.50%. At the same time, military expenditure and industry have a substantial influence of 6.37% and 2.92%, respectively. Exports of goods and services (1.08%) have the slightest effect on the gross domestic product estimation model. Our model has a tremendous potential impact on the adequacy of macroeconomic policy, providing tools that help to achieve macroeconomic and monetary stability at the global level and creating new methodological opportunities for GDP growth forecasting. The study suggests that researchers develop a deep learning model to detect and quantify company operations accurately using satellite imagery.

Keywords: Machine learning; Gross domestic product; Precision economics (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-025-10863-x

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