Động lực thu hút vốn đầu tư trực tiếp nước ngoài tại các quốc gia đang phát triển: Bằng chứng thực nghiệm từ tiếp cận học máy - Nguyen Anh , Tu (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2024)
Utilizing foreign direct investment (FDI) data from 66 developing countries for the period 2013–2021, this study implements machine learning methods, including artificial neural networks (ANN) and random forests (RF), to compare predictive quality with that of an econometric approach, the difference generalized method of moments (DGMM). In this context, DGMM analyzes the relationships between the input factors influencing FDI attraction, while ANN and RF make predictions based on statistically significant factors. The results indicate that market size, trade openness, labor abundance, and financial market development are primary drivers facilitating FDI attraction in these countries. Regarding predictive accuracy, RF exhibits the lowest error and significantly outperforms the selected methods. The findings from both the econometric model and the machine learning models are also discussed in the study.
Keywords: FDI; Forecasting; Econometrics; Machine learning; Artificial neural networks; Random forests