Bayesian and frequentist modelling of West African economic growth: a dynamic panel approach

Nureni Olawale Adeboye; Olumide Sunday Adesina

doi:https://doi.org/10.59139/stattrans-2025-029

Bayesian and frequentist modelling of West African economic growth: a dynamic panel approach

Nureni Olawale Adeboye Corresponding Author. Department of Statistics, Faculty of Basic and Applied Sciences, Osun State University, Osogbo, Osun State, Nigeria ORCID:https://orcid.org/ 0000-0002-8023-221X , Olumide Sunday Adesina School of Computing and Sciences, Nigerian University of Technology and Management, Apapa, Lagos, Nigeria & Johannesburg Business School, University of Johannesburg, South Africa ORCID:https://orcid.org/0000-0003-1476-8080 Statistics in Transition new series, vol. 26, 2025, 3, pages: 99-115 Published online: 5 September 2025 https://doi.org/10.59139/stattrans-2025-029 Citation: Adeboye N.O., Adesina O.A., 2025. Bayesian and frequentist modelling of West African economic growth: a dynamic panel approach. Statistics in Transition new series, 26(3), pp. 99-115 https://doi.org/10.59139/stattrans-2025-029

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ABSTRACT

The empirical outcomes of previous studies examining the relationship between economic growth and socio-economic indicators have been inconclusive and conflicting. To further probe into the study area, the current research employed a dynamic panel model estimated via three robust dynamic panel data estimators of the generalized method of moment (GMM), frequentist instrumental variable (IV) and the Bayesian IV on real and simulated data. Various model performance criteria such as Wald statistics, leave-out-one crossvalidation and the Pareto k checks were used for validity verification. The results of the robust diagnostics checks and a model strength metric showed that the family of IV models outperformed the GMM. Thus, the estimation provided by the Bayesian IV is upheld and recommended in modelling dynamic panel data as it provides robust estimates of the parameters of interest.

KEYWORDS

dynamic panel data, economic growth, generalized method of moment, instrumental variable, socio-economic indicators

REFERENCES

Adeboye, N. O., Abimbola, V. O., Folorunso, S. O. and Akinbo, R. Y., (2023). Modelling the volatility of African capital markets in the presence of covid-19 pandemic: evidence from the five emerging economies in Africa. Statistics in Transition new series, 24(2), pp. 17–36. https://doi.org/10.59170/stattrans-2023-018.

Adeboye, N. O., Agunbiade, D. A., (2019a). Monte Carlo estimation of heteroscedasticity and periodicity effects in a panel data regression model. International Journal of Mathematical and Computational Sciences, 13(6), pp. 157– 166.

Adeboye, N. O., Agunbiade, D. A., (2019b). Monte Carlo estimation of heterogeneity effects in a panel data regression model. International Journal of Mathematics and Computation, 30(1), pp. 62–77

Adesina, O. S, Obokoh, L. O., (2024). Bayesian modelling of tail risk using extreme value theory with application to currency exchange. International Journal of Safety and Security Engineering, 14 (5), pp. 1447–1454

Adesina, O. S., (2021). Bayesian multilevel models for count data. Journal of the Nigerian Society of Physical Sciences, 3, pp. 224–253.

Agbeyebge, T. D., Stotsky, J. and Woldemariam, A., (2006). Trade liberalization, exchange rate changes, and tax revenue in sub-Saharan Africa. Journal of Asian Economics, 17(2), pp. 261-284. https://doi.org/10.1016/j.asieco.2005.09.003.

Agiomirgianakis, G., Asteriou, D. and Monastiriotis, V., (2002). Human capital and economic growth revisited: a dynamic panel data study. International Advances in Economic Research, 8(3), pp. 177–187. https://doi.org/10.1007/BF02297955.

Ajayi, S. I., (2003). Globalization and Africa. Journal of African Economies, 12(1), pp. 120–150. https://doi.org/10.1093/jae/12.suppl_1.120.

Al-Sadom, M., Jimenez-Martin, S. and Labeaga, J., (2019). Simple methods for consistent estimation of dynamic panel data sample selection models. Barcelona GSE Working Paper Series. Working Paper ???? 1069.

Artelaris P., Arvanitidis P. and Petrakos G., (2007). Theoretical and methodological study on dynamic growth regions and factors explaining their growth performance. Paper Presented at the 2nd Workshop of DYNREG in Athens, 9–10 March.

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), pp. 277–97. JSTOR. https://doi.org/10.2307/2297968.

Arellano, M., Bover, O., (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), pp. 29–51. https://doi.org/10.1016/0304-4076(94)01642-DGet rights and content

Bardi, W., Sadraoui, T. and Bardi, A., (2016). A dynamic panel data analysis for relationship between structural policy and economic growth. International Journal of Econometrics and Financial Management, 4(1), pp. 1–10. http://pubs.sciepub.com/ijefm/4/1/1

Basu, A., Calamitsis E. A. and Ghura, D., (2005). Adjustment and growth in Sub- Saharan Africa. Economic Issue is based on IMF Working Paper 99/51.

Becsi, Z., Wang, P., (1997). Financial development and growth. Economic Review, 82(4), 46–62. https://EconPapers.repec.org/RePEc:fip:fedaer, vol. 82, no. 4.

Bengoa, M., Sanchez-Robles, B., (2003). Foreign direct investment, economic freedom and growth: new evidence from Latin America, European Journal of Political Economy, 19(3), pp. 529–545. https://doi.org/10.1016/S0176-2680(03) 00011-9.

Blundell, R., Bond, S., (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), pp. 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8.

Blundell, R., Bond, S., (2000). GMM estimation with persistent panel data: an application to production functions. Econometric Reviews, 19(3), pp. 321–340. https://doi.org/10.1080/07474930008800475.

Bürkner, P., (2017). Brms: an R package for Bayesian multilevel models using stan. Journal of Statistical Software, 80(1), pp. 1–28. https://doi.org/10.18637/jss.v080.i01.

Dreher, A., (2006). Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 38(10), pp. 1091–1110. https://doi.org/10.1080/00036840500392078.

Bun, M. J. G., Carree, M. A., (2005). Bias-corrected estimation in dynamic panel data models. Journal of Business & Economic Statistics, 23(2), pp. 200–210. http://www.jstor.org/stable/27638812.

Bascle, G., (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6(3), pp. 285–387. https://doi.org/10.1177/1476127008094339.

Cho, M., Zheng, X., (2021). Bayesian estimation of dynamic panel data gravity model. Econometric Reviews, pp. 607–634 https://doi.org/10.1080/07474938.2021.1889203.

Jiménez, J., Godinho, R., Pinto, D., Lopes, S., Castro, D., Cubero, Dm., Osorio, A., Piqué, J., Moreno-Opo, R., Quiros, P., González-Nuevo, D., Hernandez-Palacios, O. and Kéry, M., (2022). The cantabrian capercaillie: a population on the edge. Sci Total Environ. 821, 153523. https://doi.org/10.1016/j.scitotenv.2022.153523. Epub 2022 Jan 29. PMID: 35104529.

Hoffman, M. D., Gelman, A., (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. The Journal of Machine Learning Research, 15 (2014), 1593.

Jin, F., Lee, L. and Yu, J., (2021). Sequential and efficient GMM estimation of dynamic short panel data models. Econometric Reviews, pp. 1007–1037. https://doi.org/10.1080/ 07474938.2021.1889178.

Kiviet, J. F. (1995). On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), pp. 53–78. https://doi.org/10.1016/0304-4076(94)01643-E.

Lm, K. S., Pesaran, M. H. and Shin, Y., (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), pp. 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7.

Lensink, R., Morrissey, O., (2006). Foreign direct investment: flows, volatility and the impact on growth. Review of International Economics, 14(3), pp. 478–493. https://doi.org/10.1111/j.1467-9396.2006.00632.x.

Levina, O., (2011). FDI, economic freedom and growth. are they related? Being an unpublished MA, Department of Economics, Central European University.

Lichtenberg, F., (1992). R&D Investment and international productivity Differences. NBER Working Paper, no. 4161.

Li, T, Maasoumi, E, Xiao, Z., (2021). Econometric reviews honors Cheng Hsiao. Econometric Reviews, 40, 6, pp. 535–539.

Meraj, M., (2013). Impact of globalization and trade openness on economic growth in Bangladesh. Ritsumeikan Journal of Asia Pacific Studies, 31, pp. 40–50. https://doi.org/10.34409/rjaps.40.1_1.

Nkurunziza, J. D., Bates, R. H., (2003). Political institutions and economic growth in Africa. Center for International Development Working Paper, no. 98. Harvard University.

Obadan, M., (2006). Prospects for diversification in Nigeria’s export trade. Annual Conference of the Nigerian Economic Society, Heinemann press, Ibadan, pp. 33– 53.

Sharma, R., (2018). Health and economic growth: evidence from dynamic panel data of 143 years. PLoS ONE, 13(10), e0204940. https://doi.org/10.1371/journal.pone.0204940.

R Core Team, (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/>.

Yi, Hu, Dongmei, G., Ying, D. and Shouyang, W., (2014). Estimation of nonlinear dynamic panel data models with individual effects. Mathematical Problems in Engineering, Article ID 672610. http://dx.doi.org/10.1155/2014/ 672610.