SPATIAL SPILLOVERS AND MACROECONOMIC INTERDEPENDENCIES IN AFRICA: A COMPARATIVEANALYSIS OF SPATIAL PANEL ECONOMETRIC MODELS
DOI:
https://doi.org/10.33003/fjs-2025-0911-4128Keywords:
Fixed Effect, Macro-Economic Variables, Random Effect, Spatial Panel Econometric, SpilloverAbstract
This study employed spatial panel econometric techniques to analyze macroeconomic interdependencies across 49 African countries from 2010 to 2023, focusing on the role of trade balance (TB), foreign direct investment (FDI), interest rates (IR), exchange rates (EXR), and consumer price index (CPI) in influencing GDP. Traditional panel models often neglect spatial spillovers, leading to biased estimates; thus, we estimate spatial lag (SAR), spatial error (SEM), and combined SARAR models using Maximum Likelihood Estimation (MLE) and Generalized Method of Moments (GMM). The results of the analysis reveal that TB is the only consistently significant economic driver of GDP (coefficients ranging from 0.0571 to 7.0794, p < 0.01), while foreign direct investment, interest rate, exchange rate, and consumer price index show no significant effects. Spatial diagnostics confirm strong cross-country dependencies, with spatial autoregressive coefficients (0.58–1.29, p < 0.01) indicating positive spillovers and spatial error coefficients (ranging from -0.999 to 0.2569) capturing unobserved shock transmissions. Hausman tests (χ² = 3.53, p = 0.74) validate random effects specifications, suggesting unobserved regional heterogeneity is best modeled as uncorrelated with regressors. The findings underscore the necessity of spatial econometric approaches in macroeconomic analysis, particularly for policy formulations targeting trade-driven growth and regional economic integration in Africa. Policymakers should prioritize trade-enhancing strategies while accounting for spatial spillovers to maximize cross-border economic synergies.
References
models (Vol. 4). Springer Science & Business Media.
Arbia, G., Dickson, M. M., Gabriele, R., Giuliani, D., & Santi, F. (2021). On the spatial determinants of firm growth: a microlevel analysis of the Italian SMEs. Spatial economics volume II: applications, 89-120.
Baltagi BH, Song SH, Koh W (2003). “Testing Panel Data Regression Models with Spatial Error Correlation.” Journal of Econometrics, 117, 123–150.
Baltagi, B. H., & Baltagi, B. H. (2021). Spatial panel data models. Econometric Analysis of Panel Data, 391-424.
Bu, J., Du, J., & Pi, J. (2024). Do zombie firms affect healthy firms' exporting? Evidence from China. Economics of Transition and Institutional Change, 32(3), 707-738.
Bell, A. (2019). A comparison of fixed effects, random effects, and within-between models for multilevel data. Sociological Methods & Research, 51(4), 1844–1874. https://doi.org/10.1177/0049124119882463
Elhorst JP (2009). “Spatial Panel Data Models.” In MM Fischer, A Getis (eds.), Handbook of Applied Spatial Analysis. Springer-Verlag.
Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels (Vol. 479, p. 480). Heidelberg: Springer.
Elhorst, J. P. (2003). The role of spatial econometrics in economic growth theory. Journal of Economic Surveys, 35(4), 1197–1223. https://doi.org/10.1111/joes.12428
Kapoor, M., Kelejian, H. H., & Prucha, I. R. (2007). Panel data models with spatially correlated error components. Journal of econometrics, 140(1), 97-130.
Lee, L. F., & Yub, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects, Journal of Econometrics. Journal of Econometrics, 154, 165.
Lesage, J. P., & Pace, R. K. (2009). Spatial econometric models. In Handbook of applied spatial analysis: Software tools, methods and applications (pp. 355-376). Berlin, Heidelberg: Springer Berlin Heidelberg.
Mutl, J., & Pfaffermayr, M. (2011). The Hausman test in a Cliff and Ord panel model. The Econometrics Journal, 14(1), 48-76.
Ortiz, C. (2022). The environmental consequences of the shadow economy and globalization: A spatial econometric analysis. Ecological Economics, 198, 107467. https://doi.org/10.1016/j.ecolecon.2022.107467
Rosenthal, S. S., & Strange, W. C. (2004). Evidence on the nature and sources of agglomeration economies. In Handbook of regional and urban economics (Vol. 4, pp. 2119-2171). Elsevier.
Skevas, I., & Skevas, T. (2021). A generalized true random-effects model with spatially autocorrelated persistent and transient inefficiency. European Journal of Operational Research, 293(3), 1131-1142.
Youssef, A. H., Abonazel, M. R., & Shalaby, O. A. (2022). Spatial and non-spatial panel data estimators: Simulation study and application to personal income in US States. WSEAS Transactions on Mathematics, 21, 487-514.
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Copyright (c) 2025 S. A. Musa, M. O. Adenomon, B. Maijama, M. U. Adehi
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