Authors : Mukhtar Abubakar Yusuf

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/bddnfu7a

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DOI : https://doi.org/10.38124/ijisrt/25mar1083

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Abstract : This study integrates traditional econometric and advanced machine learning techniques to forecast GDP per capita. GDP, a critical indicator of economic health, reflects the monetary value of goods and services produced within a nation. Using data from 1960–2020, this study examines key macroeconomic variables such as Foreign Direct Investment (FDI) inflows, trade ratios, inflation, and Gross National Product (GNP). Ordinary Least Squares (OLS) regression was employed to quantify the relationships between these variables and GDP per capita. ARIMA and Long Short-Term Memory (LSTM) models were utilized for time-series forecasting, with an ensemble approach combining their outputs to enhance prediction accuracy. Results reveal FDI inflows and trade ratios as key drivers of GDP growth, while inflation negatively impacts economic output. The ensemble model demonstrated superior accuracy compared to individual models. This study offers actionable insights for policymakers to design strategies promoting trade, investment, and inflation control, fostering sustainable economic growth.

Keywords : GDP Per Capita Forecasting, Econometric Modeling, Machine Learning (LSTM, ARIMA) Foreign Direct Investment (FDI), Ensemble Forecasting Models.

References :

1. Alfaro, L. (2017). Gains from foreign direct investment: Macro and micro approaches. World Bank Economic Review, 30, S2–S15. https://doi.org/10.1093/wber/lhw007
2. Ball, L., Gopinath, G., Leigh, D., Mishra, P. & Spilimbergo, A. (2021). US inflation: Set for take-off? voxeu.org/article/us-inflation-set-take. http://onala.free.fr/laurenceball521.pdf
3. 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. https://doi.org/10.1016/S0022-1996(97)00033-0
4. Broadberry, S. (1992). Innovation and growth in the global economy : Gene M. Grossman and Elhanan Helpman, (MIT Press, Cambridge, MA, 1991 pp. 359). International Journal of Industrial Organization, 10(2), 323–324. https://doi.org/10.1016/0167-7187(92)90023-R
5. Chen, J. (2022). Comparison Between ARIMA Model and OLS Model Based on the Economic Representation. In BCP Business & Management MEEA (Vol. 2022). https://www.researchgate.net/publication/366297281_Comparison_Between_ARIMA_Model_and_OLS_Model_Based_on_the_Economic_Representation
6. Coibion, O., Gorodnichenko, Y. & Weber, M. (2021). NBER working paper series fiscal policy and households’ inflation expectations: evidence from a randomized control trial. https://www.washingtonpost.com/opinions/2021/02/04/larry-summers-biden-covid-stimulus/
7. Fischer, S. (1993). The role of macroeconomic factors in growth. Journal of Monetary Economics, 32(3), 485–512. https://doi.org/10.1016/0304-3932(93)90027-D
8. Fischer, S., Sahay, R. & Végh, C. A. (1996). Stabilization and Growth in Transition Economies: The Early Experience. In Journal of Economic Perspectives (Vol. 10). https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.10.2.45
9. Frankel, J. A. & Romer, D. (1999). Does trade cause growth? American Economic Review, 89(3), 379–399. https://doi.org/10.1257/aer.89.3.379
10. Guo, N., Chen, W., Wang, M., Tian, Z. & Jin, H. (2021). Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT). Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/6610273
11. Mankiw, N. G. & Taylor, M. P. (2020). New fifth edition of Economics . HOA SEN University Digital Library, Fifth Edition. https://thuvienso.hoasen.edu.vn/handle/123456789/11992?show=full
12. O. D. Anderson. (1977). Time Series Analysis and Forecasting: Another Look at the Box-Jenkins Approach. Journal of the Royal Statistical Society: Series D (The Statistician), 26(4). https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2987813
13. PM Romer. (1990). Endogenous technological change. Journal of Political Economy, 98(Volume 98, Number 5, Part 2). https://www.journals.uchicago.edu/doi/abs/10.1086/261725
14. Savignac, F., Gautier, E., Gorodnichenko, Y. & Coibion, O. (2022). Firms’ Inflation Expectations: New Evidence from France. www.iza.org
15. Siami-Namini, S., Tavakoli, N. & Siami Namin, A. (2018). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
16. Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., Jiang, L. & Cheng, Z. (2020). Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186, 106682. https://doi.org/10.1016/J.PETROL.2019.106682
17. Zhang, S., Yao, L., Sun, A. & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. In ACM Computing Surveys (Vol. 52, Issue 1). Association for Computing Machinery. https://doi.org/10.1145/3285029