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

Аннотация

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 Key words: Econometric Analysis, Foreign Direct Investment (FDI), Artificial Intelligence (AI), Neural Networks, Machine Learning Forecasting Models, Investment Flows, Economic Indicators, Predictive Modeling, Data-Driven, Decision Making, Investment Attraction Strategies Introduction: Foreign Direct Investment (FDI) is an important contributor to a developing country’s economy, particularly emerging and developing economies. The increasing level of globalization also means that attracting foreign direct investment has become one of the major policy considerations of governments as they strive to stimulate economic growth, create jobs, and promote technological innovations. 11 Acumen: International Journal of Multidisciplinary Research Acumen: International Journal of Multidisciplinary Research ISSN: 3060-4745 IF(Impact Factor)10.41 / 2024 Volume 2, Issue 5 Forecasting inflows of FDI remains a highly problematic endeavor since the complexity and multifaceted nature of the investment decision-making process is often related to macroeconomic, political, regulatory, and domestic market variables. Economometric methods are in many cases used to forecast FDI, but typically lack the ability to account for the non-linear connections and dynamic interactions between the variables that are more important to investing across countries. Recent advances in artificial intelligence (AI), especially machine learning techniques such as neural networks, have shown great promise in reaching these limits. Neural networks can capture both complex interactions between variables across large data sets, thereby providing more accurate and dynamic predictions of future trends. Such AI-powered models can take advantage of vast amounts of historical data as well as macroeconomic indicators to provide predictive information on FDI inflows with greater accuracy. This article proposes an integrated use of econometric analysis with artificial intelligence (AI) methods such as neural networks to forecast FDI inflows. More traditional economic models can be used to interpret and predict FDI patterns. However, instead of adopting new models based on large data sets, they implement model evaluation strategies that are flawed in terms of uncertainty and process convergence. Therefore, new predictive models that use neural networks to take into account historical data as well as relevant economic indicators could provide more efficient and efficient performance. Main body: Foreign Direct Investment (FDI) is one of the most important types of external capital of development for developing countries. FDI supplies critical capital for efficient transfer of technology, creates jobs, and promotes economic growth. There are many factors that influence decision-making about FDI inflows, including economic conditions, regulatory environment, market potential, geopolitical considerations, etc. Economists have developed several models to explain these flows. A leading theory is the OLI model (Ownership, Location and Internalization). OLI emphasizes that firms decide to invest in foreign markets when they have ownership advantages (such as technological advantages or brand reputation), have strategic advantage (such as access to markets or low costs), and they internalize their transactions in order to reduce transaction costs (such as avoidance of market imperfections). 12 Acumen: International Journal of Multidisciplinary Research Acumen: International Journal of Multidisciplinary Research ISSN: 3060-4745 IF(Impact Factor)10.41 / 2024 Volume 2, Issue 5 But even with the theoretical improvements made in understanding FDI dr