ECONOMETRIC ANALYSIS AND FORECASTING OF FDI INFLOWS USING NEURAL NETWORKS (AI)

Abstract

This article presents a comprehensive econometric analysis and forecasting of Foreign Direct Investment (FDI) inflows using artificial intelligence (AI) techniques, specifically focusing on the application of neural networks. As global investment patterns become more complex, traditional econometric models often fall short in capturing nonlinear relationships and predicting future trends. By leveraging machine learning algorithms, this study addresses these challenges, offering a more robust and dynamic method for forecasting FDI. The research utilizes historical data, macroeconomic indicators, and country-specific variables to train neural networks, aiming to enhance the precision of FDI inflow predictions. The results demonstrate the superior performance of AI-driven models in capturing the underlying trends of investment flows compared to conventional econometric models. The findings suggest that AI and machine learning can significantly improve investment decision-making processes, making it easier for governments, policymakers, and businesses to plan and adapt to changing global investment environments. The study concludes by emphasizing the importance of integrating AI technologies into economic forecasting and highlights their potential to transform FDI analysis and policy development in emerging and developed economies alike