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
DOI:
https://doi.org/10.24311/jabes/2024.35.5.6Từ khóa:
FDI, Dự báo, Kinh tế lượng, Học máy, Mạng nơ-ron nhân tạo, Rừng ngẫu nhiênTóm tắt
Sử dụng dữ liệu về đầu tư trực tiếp nước ngoài (FDI) của 66 quốc gia đang phát triển giai đoạn 2013–2021, nghiên cứu này triển khai các phương pháp học máy bao gồm: Mạng nơ-ron nhân tạo (Artificial Neural Networks – ANN) và rừng ngẫu nhiên (Random Forest – RF) nhằm so sánh chất lượng dự báo với một phương pháp tiếp cận kinh tế lượng là GMM sai phân (Differenced GMM – DGMM). Trong đó, DGMM phân tích mối liên hệ giữa các nhân tố đầu vào ảnh hưởng tới thu hút FDI, trong khi phương pháp mạng nơ-ron nhân tạo và phương pháp rừng ngẫu nhiên dựa vào các nhân tố có ý nghĩa thống kê để đưa ra dự báo. Kết quả cho thấy các nhân tố độ lớn thị trường, độ mở thương mại, mức độ dồi dào của lao động, sự phát triển của thị trường tài chính là những nhân tố chính trợ lực cho thu hút FDI tại các quốc gia này. Trong khi đó, xét về mặt dự báo, sai số của phương pháp rừng ngẫu nhiên là nhỏ nhất và vượt trội so với các phương pháp so sánh. Các phát hiện của mô hình kinh tế lượng và mô hình học máy cũng được thảo luận trong nghiên cứu.
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