Sains Malaysiana 54(9)(2025): 2287-2300

http://doi.org/10.17576/jsm-2025-5409-15

 

Modeling of Gross Domestic Product with Foreign Direct Investment using
Lotka-Volterra Equations

(Pemodelan Keluaran Dalam Negara Kasar dengan Pelaburan Langsung Asing menggunakan Persamaan Lotka-Volterra)

 

MOHAMMAD KHATIM HASAN*, NOOR ASHIKIN OTHMAN & BAHARI IDRUS

 

Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

 

Received: 31 December 2024/Accepted: 9 July 2025

 

Abstract

This paper investigates the dynamic interaction between Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) using two distinct numerical methods: The Fourth-Order Runge-Kutta (RK4) method on Lotka-Volterra model and a family of Least-Squares (LS) methods. The study aims to provide a comparative analysis of these methods in terms of their accuracy, efficiency, and applicability in modeling the complex relationship between GDP and FDI. The RK4 method is employed to model the dynamic system governing the interaction between GDP and FDI. This method is chosen for its robustness in handling non-linear systems and its ability to provide precise numerical solutions with minimal computational error. On the other hand, the least squares method provides a static approximation by fitting a linear or nonlinear relationship between GDP and FDI. The paper conducts simulations using real-world data on GDP and FDI from Malaysia spanning the years 2009 to 2020. The results obtained from both methods are compared to assess their performance. The RK4 method on Lotka-Volterra model demonstrates superior accuracy in capturing the dynamic behavior of the GDP-FDI interaction, particularly in scenarios involving rapid changes or non-linear dynamics.

 

Keywords: Dynamic interaction; Foreign Direct Investment (FDI); Fourth-Order Runge-Kutta (RK4); Gross Domestic Product (GDP); Lotka-Volterra (LV)

 

Abstrak

Kertas ini mengkaji interaksi dinamik antara Keluaran Dalam Negara Kasar (KDNK) dan Pelaburan Langsung Asing (PLA) dengan menggunakan dua kaedah berangka yang berbeza: Kaedah Runge-Kutta Tertib Keempat (RK4) pada model Lotka-Volterra dan satu keluarga kaedah Kuasa Dua Terkecil. Kajian ini bertujuan untuk memberikan analisis perbandingan antara kaedah ini dari segi ketepatan, kecekapan dan kebolehgunaan dalam memodelkan hubungan kompleks antara KDNK dan PLA. Kaedah RK4 digunakan untuk memodelkan sistem dinamik yang mengawal interaksi antara KDNK dan PLA. Kaedah ini dipilih kerana keteguhannya dalam mengendalikan sistem bukan linear dan keupayaannya memberikan penyelesaian berangka yang tepat dengan kesilapan pengiraan yang minimum. Sebaliknya, kaedah kuasa dua terkecil memberikan suatu anggaran statik dengan memadankan hubungan linear atau tak linear antara KDNK dan PLA. Kertas ini menjalankan simulasi menggunakan data dunia sebenar KDNK dan PLA dari Malaysia bagi tempoh 2009 hingga 2020. Hasil yang diperoleh daripada kedua-dua kaedah ini dibandingkan untuk menilai prestasi mereka. Kaedah RK4 pada model Lotka-Volterra menunjukkan ketepatan yang lebih tinggi dalam mencerap tingkah laku dinamik interaksi KDNK-PLA, terutamanya dalam senario yang melibatkan perubahan pantas atau dinamik bukan linear.

 

Kata kunci: Interaksi dinamik; Keluaran Dalam Negara Kasar (KDNK); Lotka-Volterra (LV); Pelaburan Langsung Asing (PLA); Runge-Kutta Tertib Keempat (RK4)

REFERENCES

Agosin, M.R. & Mayer, R. 2000. Foreign investment in developing countries: Does it crowd in domestic investment? Oxford Development Studies 28(2): 149-162.

Alfaro, L., Chanda, A., Kalemli-Ozcan, S. & Sayek, S. 2004. FDI and economic growth: The role of local financial markets. Journal of International Economics 64(1): 89-112.

Ang, J.B. 2008. Determinants of foreign direct investment in Malaysia. Journal of Policy Modeling 30(1): 185-189.

Borensztein, E., De Gregorio, J. & Lee, J.W. 1998. How does foreign direct investment affect economic growth? Journal of International Economics 45(1): 115-135.

Campbell, J.Y., Lo, A.W. & MacKinlay, A.C. 1997. The Econometrics of Financial Markets. Princeton: Princeton University Press.

De Mello, L.R. 1997. Foreign direct investment in developing countries and growth: A selective survey. Journal of Development Studies 34(1): 1-34.

Department of Statistics of Malaysia. 2023. [Online Database].

Fox, J. 2016. Applied Regression Analysis and Generalized Linear Models. 3rd ed. Sage Publications.

Görg, H. & Greenaway, D. 2004. Much ado about nothing? Do domestic firms really benefit from foreign direct investment? The World Bank Research Observer 19(2): 171-197. https://doi.org/10.1093/wbro/lkh019

Gui-Diby, S.L. 2014. Impact of foreign direct investments on economic growth in Africa: Evidence from three decades of panel data analyses. Research in Economics 68(3): 248-256. https://doi.org/10. 1016/j.rie.2014.04.003

Hasan, M.K., Othman, N.A. & Idrus, B. 2022. Data-driven macro-economic model analysis using non-standard trimean algorithm. In Intelligent Systems Modeling and Simulation II. Studies in Systems, Decision and Control, edited by Abdul Karim, S.A. Springer, Cham. p. 444. https://doi.org/10.1007/978-3-031-04028-3_9

Hasan, M.K., Othman, N.A. & Idrus, B. 2019. Relationship analysis of Malaysian gross domestic product and foreign direct investment using numerical method with optimization approach. International Journal of Advanced Science and Technology 28(16): 410-416.

Hasan, M.K., Karim, S.A.A. & Sulaiman, J. 2015. Graphical analysis of Rosenzweig-MacArthur model via adams-moultan and fourth order Runge-Kutta methods. Proceedings of the 2015 International Conference on Electrical Engineering and Informatics (ICEEI), Denpasar, Indonesia. pp. 670-675. doi: 10.1109/ICEEI.2015.7352583

Helene, O., Mariano, L. & Guimarães-Filho, Z. 2016. Useful and little-known applications of the Least Square Method and some consequences of covariances. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 833: 82-87. https://doi.org/10.1016/j.nima.2016.06.126

Hirsch, M.W., Smale, S. & Devaney, R.L. 2013. Differential Equations, Dynamical Systems, and an Introduction to Chaos. 3rd ed. Massachusetts: Academic Press.

Hussain, K., Ismail, F. & Senu, N. 2016. Solving directly special fourth-order ordinary differential equations using Runge–Kutta type method. Journal of Computational and Applied Mathematics 306: 179-199. https://doi.org/10.1016/j.cam.2016.04.002

Joo, B.A. & Shawl, S. 2023. Understanding the relationship between foreign direct investment and economic growth in BRICS: Panel ARDL Approach. VIKALPA The Journal for Decision Makers 48(2): 100-113.

Lean, H.H. & Tan, B.W. 2011. Linkages between foreign direct investment, domestic investment and economic growth in Malaysia. Journal of Economic Cooperation and Development 32(4): 75-96.

Mohamed, M.R., Jit Sing, K.S. & Liew, C.Y. 2013. Impact of foreign direct investment & domestic investment on economic growth of Malaysia. Malaysian Journal of Economic Studies 50(1): 21-35.

Montgomery, D.C., Peck, E.A. & Vining, G.G. 2021. Introduction to Linear Regression Analysis. 6th ed. New York: John Wiley & Son.

Mustafa, T. & Idris, A.R. 2021. The foreign direct investment and economic growth in Malaysia: An OLS approach. Journal of Contemporary Social Science Research 5(1): 30-39.

Mwakabungu, B.H.P. & Kauangal, J. 2023. An empirical analysis of the relationship between FDI and economic growth in Tanzania. Cogent Economics & Finance 11(1): 2204606. https://doi.org/10.1080/23322039.2023.2204606

Mwinuka, L. & Mwangoka, V.C. 2023. Manufacturing sector’s growth in Tanzania: Empirical lessons from macroeconomic factors, 1970–2021. Cogent Economics & Finance 11: 1. https://doi.org/10.1080/23322039.2023.2223419

Nguyen, M.L.T. 2022. Foreign direct investment and economic growth: The role of financial development. Cogent Business & Management 9(1): 2127193. DOI: 10.1080/23311975.2022.2127193

Pineiro, G., Perelman, S., Guerschman, J.P. & Paruelo, J.M. 2008. How to evaluate models: Observed vs predicted or predicted vs. observed? Ecological Modelling 216: 316-322.

Ratkowsky, D.A. 1983. Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker.

Reddy, T.A. & Henze, G.P. 2023. Linear regression analysis using least squares. In Applied Data Analysis and Modeling for Energy Engineers and Scientists. Springer, Cham. pp. 169-221. https://doi.org/10.1007/978-3-031-34869-3_5

Seber, G.A.F. & Wild, C.J. 2003. Nonlinear Regression. New York: John Wiley & Sons Inc.

Sijabat, R. 2023. The association between foreign investment and gross domestic product in ten ASEAN countries. Economies 11(7): 188. https://doi.org/10.3390/ economies11070188

Sima, D.M., Van Huffel, S. & Golub, G.H. 2004. Regularized total least squares based on quadratic eigenvalue problem solvers. Bit Numer. Math. 44: 793-812. https://doi.org/10.1007/s10543-004-6024-8

Sumner, A. 2005. Is foreign direct investment good for the poor? A review and stocktake. Development in Practice 15(3-4): 269-285. https://doi.org/10.1080/ 09614520500076183

Wang, D., Yin, J., Tang, T., Chen, X. & Wu, Z. 2018. Quadratic constrained weighted least-squares method for TDOA source localization in the presence of clock synchronization bias: Analysis and solution. Digital Signal Processing 82: 237-257. https://doi.org/10.1016/j.dsp.2018.08.002

Wooldridge, J.M. 2016. Introductory Econometrics: A Modern Approach. 6th ed. Massachusetts: Cengage Learning.

Wu, L. & Liu, S. 2013. Using grey Lotka-Volterra model to analyze the relationship between the gross domestic products and the foreign direct investment of Ningbo city. Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). pp. 265-268.

 

*Corresponding author; email: mkh@ukm.edu.my

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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