Deep learning-based neural network modeling for economic performance prediction: An empirical study on Iraq
Loading...
Date
2025-02-20
Journal Title
Journal ISSN
Volume Title
Publisher
Peninsula Publishing Press
Abstract
This study investigates the application of deep learning-based neural networks for predicting Iraq’s economic performance. Traditional econometric models impose restrictive assumptions that limit their predictive accuracy, especially in volatile economic environments. To overcome these limitations, we propose an artificial neural network (ANN) model trained on six key macroeconomic indicators: Gross Domestic Product (GDP), inflation rate, unemployment rate, exchange rate, trade volume, and government spending. The dataset spans from 2000 to 2023, sourced from authoritative economic institutions. The methodology incorporates feature scaling, hyperparameter tuning, and backpropagation optimization to minimize mean squared error (MSE) and enhance generalization performance. The model is validated through cross-validation and out-of-sample testing. Descriptive statistical analysis highlights the variability of macroeconomic indicators, while the ANN model effectively captures nonlinear dependencies. The results indicate that GDP and government spending are the most influential factors in economic performance prediction, while unemployment rate and exchange rate exhibit lower predictive significance. The model demonstrates superior accuracy compared to traditional regression-based approaches, with minimal error in both training and testing phases. This research contributes to the empirical literature on machine learning in economic forecasting by presenting a robust alternative to conventional predictive models. The findings provide policymakers with valuable insights for designing data-driven economic policies. Future work should explore hybrid models integrating deep learning with traditional econometrics to improve interpretability while maintaining predictive power.
Description
This paper examines use of deep learning neural networks to predict economic performance using macroeconomic data from 2000 to 2023. An artificial neural network model is trained on GDP inflation unemployment exchange rate trade volume and government spending to overcome limits of traditional econometric models. Results show higher prediction accuracy with GDP and government spending as key drivers while unemployment and exchange rate are less influential. The study supports SDG 8 decent work and economic growth SDG 9 industry innovation and infrastructure and SDG 16 strong institutions. It aligns with Uganda National Development Plan IV aspirations on digital transformation nationwide.
Keywords
Deep Learning, Economic Forecasting, Neural Networks, Macroeconomic, Prediction
Citation
Shaker, A. S., Ali, G., Wamusi, R., & Habib, H. (2025). Deep learning-based neural network modeling for economic performance prediction: An empirical study on Iraq. EDRAAK, 2025, 47-56.