ASSESSING THE IMPACT OF EU INTEGRATION ON ECONOMIC GROWTH FORECASTS USING ARTIFICIAL NEURAL NETWORK MODELS - Caushi, Andi (Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 2025)
This study investigates the interplay of endogenous and exogenous factors influencing GDP growth in Albania and Serbia, two nations at different stages of EU integration. Key variables examined include consumption, education, exports, foreign direct investment (FDI), imports, savings (all relative to GDP), and urbanization rates, with a focus on the impact of high-technology exports. To analyze the World Bank dataset from 1993 to 2023 at a 95% confidence level, this study employs an Artificial Neural Network-Multilayer Perceptron Model (ANN-MLP), designated as H (1;1). The dataset is systematically divided into two subsets: the first subset comprises 70% of the observations, while the second subset contains the remaining 30%. The initial subset is utilized to optimize forecasting accuracy, allowing the model to adjust its parameters accordingly. Once the parameters have been optimized, they are applied to the second subset to assess the model's performance. This procedure yields a forecast error of less than 0.3%, demonstrating the model's efficacy. The novelty of this study lies in forecasting GDP growth in Albania and Serbia using ANN-MLP models, with a particular focus on the role of high-technology exports. It emphasizes the distinct importance of exports in Albania versus consumption and FDI in Serbia, offering nuanced insights into country-specific growth mechanisms. This tailored, context-sensitive approach provides valuable implications for policymakers aiming for EU integration, filling a gap in existing literature by explicitly contrasting the economic drivers in these neighboring, yet economically distinct, national contexts.