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/2024.35.5.6Keywords:
FDI, Forecasting, Econometrics, Machine learning, Artificial neural networks, Random forestsAbstract
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.
References
Aghion, P., Howitt, P. (1998). Endogenous Growth Theory. Cambridge, MA: MIT Press.
Alon, I., Bretas, V. P., Sclip, A., & Paltrinieri, A. (2022). Greenfield FDI attractiveness index: A machine learning approach. Competitiveness Review: An International Business Journal, 32(7), 85-108.
Arel-Bundock, V. (2017). The political determinants of foreign direct investment: A firm-level analysis. International Interactions, 43(3), 424-452.
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.
Asiedu, E., & Lien, D. (2004). Capital controls and foreign direct investment. World Development, 32(3), 479-490.
Baltas, N., Tsionas, M. G., & Baltas, K. (2018). Foreign direct investment in OECD countries: A special focus in the case of Greece. Applied Economics, 50(52), 5579-5591.
Basu, R., & Ferreira, J. (2020). Understanding household vehicle ownership in Singapore through a comparison of econometric and machine learning models. Transportation Research Procedia, 48, 1674-1693.
Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. In Montavon, G., Orr, G.B., Müller, KR. (eds), Neural Networks: Tricks of the Trade (2nd ed.) (pp. 437-478). Berlin: Springer.
Bermejo, S., & Cabestany, J. (2001). Oriented principal component analysis for large margin classifiers. Neural Networks, 14(10), 1447-1461.
Beyene, J., Atenafu, E. G., Hamid, J. S., To, T., & Sung, L. (2009). Determining relative importance of variables in developing and validating predictive models. BMC Medical Research Methodology, 9(64), 1-10.
Borup, D., Christensen, B. J., Mühlbach, N. S., & Nielsen, M. S. (2023). Targeting predictors in random forest regression. International Journal of Forecasting, 39(2), 841-868.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
Cheng, L., & Kwan, Y. K. (2000). The location of foreign direct investment in Chinese regions: Further analysis of labor quality. In Ito, T., Krueger, A. O. (Ed.), The Role of Foreign Direct Investment in East Asian Economic Development (pp. 213-238). University of Chicago Press.
Chronopoulos, I., Chrysikou, K., Kapetanios, G., Mitchell, J., & Raftapostolos, A. (2023). Deep neural network estimation in panel data models. FRB of Cleveland Working Paper No. 23-15. Available at SSRN: https://ssrn.com/abstract=4501438
Clements, M. P., & Hendry, D. F. (2001). Forecasting Non-Stationary Economic Time Series. The MIT Press.
Coughlin, C. C., & Segev, E. (2000). Foreign direct investment in China: A spatial econometric study. The World Economy, 23(1), 1-23.
Crescenzi, R., Ganau, Roberto, & Storper, M. (2022). Does foreign investment hurt job creation at home? The geography of outward FDI and employment in the USA. Journal of Economic Geography, 22(1), 53-79.
Darken, C., & Moody, J. (1991). Towards faster stochastic gradient search. In Proceedings of the 4th International Conference on Neural Information Processing Systems (NIPS'91). Morgan Kaufmann Publishers Inc. (pp. 1009-1016), San Francisco, CA, USA.
Dorakh, A. (2020). FDI determinants in the european union: Empirical application. Scientific Annals of Economics and Business, 67(2), 219-232.
Dunning, J. H. (1973). The determinants of international production. Oxford Economic Papers, 25(3), 289-336.
Dunning, J. H. (1980). Toward an eclectic theory of international production: Some empirical tests. Journal of International Business Studies, 11, 9-31.
Dunning, J. H., & Lundan, S. M. (2008). Multinational Enterprises and the Global Economy. Edward Elgar Publishing.
Gasanova, A., Medvedev, A. N., & Komotskiy, E. I. (2017). The assessment of corruption impact on the inflow of foreign direct investment. AIP Conference Proceedings, 1836(1). https://doi.org/10.1063/1.4981951
Gujarati, D. N., & Porter, D. C. (2009). Basic Econometric. McGraw-Hill Irwin.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
Head, K., & Ries, J. (1996). Inter-city competition for foreign investment: Static and dynamic effects of China's incentive areas. Journal of Urban Economics, 40(1), 38-60.
Hecht-Nielsen, R. (1987). Kolmogorov’s mapping neural network existence theorem. Paper presented at the Proceedings of the international conference on Neural Networks (pp. 11-13), San
Diego, CA.
Hunter, D., Yu, H., Pukish III, M. S., Kolbusz, J., & Wilamowski, B. M. (2012). Selection of proper neural network sizes and architectures – A comparative study. IEEE Transactions on Industrial Informatics, 8(2), 228-240.
Keller, W. (2021). Knowledge spillovers, trade, and FDI (NBER Working Paper 28739). Retrieved from the National Brueau of Economic Research: https://www.nber.org/papers/w28739
Kojima, K., & Ozawa, T. (1985). Toward a theory of industrial restructuring and dynamic comparative advantage. Hitotsubashi Journal of Economics, 26(2), 135-145.
Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random forest. In Liu, B., Ma, M., Chang, J. (eds), Information Computing and Applications. ICICA 2012, Lecture Notes in Computer Science, 7473, Berlin, Heidelberg: Springer.
Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631-652.
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. Ieee Access, 8, 150199-150212.
Narula, R., & Dunning, J. H. (2000). Industrial development, globalization and multinational enterprises: New realities for developing countries. Oxford Development Studies, 28(2), 141-167.
Neugebauer, M., & Schindler, S. (2012). Early transitions and tertiary enrolment: The cumulative impact of primary and secondary effects on entering university in Germany. Acta Sociologica, 55(1), 19-36.
Nguyen, A.-T., Lu, S.-H., & Nguyen, P. T. T. (2021). Validating and forecasting carbon emissions in the framework of the environmental Kuznets curve: The case of Vietnam. Energies, 14(11), 3144.
Nguyen, A.-T., Anwar, S., Alexander, W. R. J., & Lu, S.-H. (2022). Openness to trade, foreign direct investment, and economic growth in Vietnam. Applied Economics, 54(29), 3373-3391.
Nguyen, C. P., & Lee, G. S. (2021). Uncertainty, financial development, and FDI inflows: Global evidence. Economic Modelling, 99, 105473.
Popovici, O. C., Călin, A. C., Ivana, D., & Sorin, D. (2021). FDI determinants revisited: Extensive evidence. Romanian Journal of Economic Forecasting, 24(4), 103-123.
Rodriguez, X. A., & Pallas, J. (2008). Determinants of foreign direct investment in Spain. Applied Economics, 40(19), 2443-2450.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408.
Sameen, M. I., Pradhan, B., & Lee, S. (2019). Self-learning random forests model for mapping groundwater yield in data-scarce areas. Natural Resources Research, 28, 757-775.
Schaul, T., Zhang, S., & LeCun, Y. (2013). No more pesky learning rates. Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013.
Shobana, G., & Umamaheswari, K. (2021). Forecasting by machine learning techniques and econometrics: A review. Paper presented at the 2021 6th international Conference On Inventive Computation Technologies (ICICT), Coimbatore, India, 20-22 January 2021. https://ieeexplore.ieee.org/document/9358514
Singh, D. (2021). Interpretable machine-learning approach in estimating FDI inflow: Visualization of ML models with LIME and H2O. TalTech Journal of European Studies, 11(1), 133-152.
Solow, R. M. (1999). Neoclassical growth theory. Handbook of Macroeconomics, 1, 637-667.
Steinbuks, J. (2019). Assessing the accuracy of electricity production forecasts in developing countries. International Journal of Forecasting, 35(3), 1175-1185.
Takase, T., Oyama, S., & Kurihara, M. (2018). Effective neural network training with adaptive learning rate based on training loss. Neural Networks, 101, 68-78.
Teixeira, A. A., & Nascimento, S. (2019). FDI in small states and unattractive markets: The case of São Tomé and Príncipe. Journal of Contemporary African Studies, 37(4), 351-378.
Traore, Y., Muchie, M., & Worku, Z. (2022). South African ambitious investment objectives: The current foreign direct investment determinants in South Africa. African Journal of Science, Technology, Innovation and Development, 14(1), 31-48.
Tu, N. A. (2024). Innovation and foreign direct investment attraction in developing countries. Cogent Economics & Finance, 12(1), 2312386.
Turban, E., Sharda, R., & Delen, D. (2011). Decision Support and Business Intelligence Systems (9th ed.). Pearson Education India.
Vandeginste, B. G. M., Massart, D. L., Buydens, L. M. C., De Jong, S., Lewi, P. J., & Smeyers-Verbeke, J. (1998). Artificial neural networks. In B. G. M. Vandeginste, D. L. Massart, L. M. C. Buydens, S. De Jong, P. J. Lewi, & J. Smeyers-Verbeke (Eds.), Data Handling in Science and Technology, 20 (pp. 649-699). Elsevier.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988-999.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Deep learning. In I. H. Witten, E. Frank, M. A. Hall, & C. J. Pal (Eds.), Data Mining (4th ed.) (pp. 417-466). Morgan Kaufmann.
Yao, S. (2006). On economic growth, FDI and exports in China. Applied Economics, 38(3), 339-351.
Yoon, J. (2021). Forecasting of real GDP growth using machine learning models: Gradient boosting and random forest approach. Computational Economics, 57(1), 247-265.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
