Predictive Modeling of Ghana’s Economic Growth: a Compar‎ative Analysis of Support Vector Regressionand Ordinary Least Squares Regression UsingForeign Direct Investment Data

  • Authors

    • Yeboah Andrews Yeboah Department of Applied Mathematics and Statistics, Accra Technical University https://orcid.org/0009-0004-5012-288X
    • Richard Nkrumah Department of Applied Mathematics and Statistics, Accra Technical University https://orcid.org/0000-0002-2308-7650
    • Edwin Mends-Brew Department of Applied Mathematics and Statistics, Accra Technical University
    • Angela Nkrumah Ghana Health Service https://orcid.org/0000-0002-4623-6903
    • Boakye Agyemang Koforidua Technical University, Faculty of Applied Sciences and Technology, Applied Mathematics Department
    • Abdul-Basit Danjoe Munkaila Department of Logistics and Procurement Management, Faculty of Business, Tamale Technical University
    https://doi.org/10.14419/85ej6036

    Received date: November 24, 2025

    Accepted date: January 9, 2026

    Published date: January 12, 2026

  • Foreign Direct Investment; Economic Growth; Predictive Modeling; Ordinary Least Squares Regression; Support Vector Machine

  • Abstract

    The contribution of Foreign Direct Investment (FDI) to economic growth remains a central issue in development economics, particularly for ‎emerging economies that are undergoing macroeconomic adjustment. This study examines the relationship between FDI and Ghana’s ‎Gross Domestic Product (GDP) growth by conducting a comparative assessment of Ordinary Least Squares Regression (OLSR) and Support Vector Regression (SVR). Annual data spanning 1996–2023 were obtained from the World Bank and OECD national accounts databases. Model performance was evaluated using out-of-sample Root Mean Square Error (RMSE) and coefficient of determination (R²) to ‎distinguish predictive accuracy from explanatory power. The results indicate that FDI exerts a statistically significant and positive effect on ‎GDP growth under the OLSR framework, with an R² value of 0.47, suggesting moderate explanatory strength. In contrast, the SVR model ‎, implemented with a radial basis function kernel and tuned via cross-validation, achieved a marginally lower prediction error (RMSE = 0.754) ‎, but lower explanatory power (R² = 0.32). These findings highlight a clear trade-off between predictive accuracy and interpretability. The study emphasizes that the single-predictor specification serves a methodological purpose rather than a comprehensive economic representation, and that results should be interpreted within this constrained framework. Generally, the analysis shows the complementary roles of machine ‎learning and econometric approaches in applied economic modeling and provides evidence-based guidance for model selection in macroeconomic forecasting and policy analysis in Ghana‎.

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