STATISTICAL APPROACHES IN FORECASTING FOREIGN DIRECT INVESTMENT FLOWS

Authors

  • Akhmedova Mavluda Associate Professor of the department of Management, International School of Finance Technology and Science (ISFT institute), Uzbekistan

Keywords:

Foreign Direct Investment (FDI), Forecasting, Econometrics, ARIMA, VAR, Cointegration, Machine Learning, Bayesian Models, Statistical Modeling, Economic Forecasting, Hybrid Models, Data Analytics.

Abstract

Foreign Direct Investment (FDI) plays a critical role in the economic development and globalization of national economies. Accurate forecasting of FDI flows is essential for policymakers, investors, and economic planners. This paper explores the application of advanced statistical methods in forecasting FDI inflows and outflows. The study reviews traditional econometric models such as Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and Cointegration Analysis, as well as modern approaches including Machine Learning algorithms and Bayesian forecasting models. Empirical data are analyzed to compare the forecasting accuracy of these models across various economic conditions and countries. The results highlight the strengths and limitations of each method, demonstrating that hybrid models combining statistical and machine learning techniques often yield higher forecasting precision. The paper concludes with policy implications and recommendations for enhancing FDI forecasting practices through the integration of statistical innovations and real-time data analytics.

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Published

2025-06-17

How to Cite

Akhmedova Mavluda. (2025). STATISTICAL APPROACHES IN FORECASTING FOREIGN DIRECT INVESTMENT FLOWS. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE & INTERDISCIPLINARY RESEARCH ISSN: 2277-3630 Impact Factor: 8.036, 14(06), 115–123. Retrieved from https://www.gejournal.net/index.php/IJSSIR/article/view/2676