An Optimized ARDL Model for Estimating and Forecasting the Dynamic Relationship between Public Revenues and Expenditures
DOI:
https://doi.org/10.62933/pc26fc02Keywords:
ARDL, ARDL-GWO, ARDL-WOA, Public Revenues, Public Expenditures, Metaheuristic AlgorithmsAbstract
Public revenues constitute the primary source for financing government expenditures and fulfilling fiscal responsibilities, and they play a crucial role in influencing a country's economic growth. This study examines the relationship between public revenues and public expenditures by integrating the classical Autoregressive Distributed Lag (ARDL) model with two artificial intelligence-based optimization algorithms: the Grey Wolf Optimizer (GWO) and the Whale Optimization Algorithm (WOA). These nature-inspired metaheuristic algorithms mimic the cooperative hunting strategies of grey wolves and humpback whales, respectively. The integration of these algorithms with the ARDL framework aims to enhance parameter estimation, particularly when nonlinear patterns may exist in the relationship between variables, while also relaxing some of the restrictive assumptions associated with the Ordinary Least Squares (OLS) estimation method commonly used in ARDL models. Empirical findings reveal that the ARDL–GWO model outperforms both ARDL–OLS and ARDL–WOA, achieving the lowest Root Mean Square Error (RMSE) and the highest coefficient of determination (R²), which confirms its effectiveness in improving the accuracy of parameter estimation.
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Copyright (c) 2026 Sulaiman Hussien Jawad, Munaf Yousif Hmood (Author)
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