FORECASTING NIGERIAN ECONOMIC GROWTH BASED CORPORATE INCOME TAX
DOI:
https://doi.org/10.46754/jmsi.2022.12.005Keywords:
Artificial neural network, Corporate income tax, Economic growth, Forecasting, Multiple regressionsAbstract
Developing countries like Nigeria have been hit hard by recent economic crises, and uncertainty is one of the characteristics that all prices and revenues share. A forecast of Nigerian economic growth was attempted using an artificial neural network (ANN) and statistical model to analyse the contribution of non-oil income tax generation to Nigerian economic development. While the primary objective is to develop and implement the proposed models capable of simulating a real non-oil income tax and evaluate their performances. The dataset (Corporate Income Tax) from 2015–2020 was obtained from the National Bureau of Statistics of Nigeria (NBSN). Three training algorithms for ANN were adopted, such as conjugate gradient back-propagation with Fletcher-Reeves restarts, Bayesian regularisation, and gradient descent with an adaptive learning rate, whereas in the statistical part, multiple linear regressions were applied. Comparing all the models revealed that the Bayesian regularisation produced more accurate results than the other models.
References
McIver, W. (2006). Community informatics and human development. In Meersman, R., Tari, Z., Herrero, P. (Eds.), On the move to meaningful internet systems 2006: OTM 2006 Workshops. OTM 2006. Lecture notes in computer science (Vol 4277). Berlin, Heidelberg: Springer. https://doi.org/10.1007/11915034_38
Bohanon, C. E., Horowitz, J. B., & McClure, J. E. (2014). Saying too little, too late: Public finance textbooks and the excess burdens of taxation. Econ Journal Watch, 11(3), 277–296.
Algoni, U. S., & Agrawwal, P. K. (2017). An assessment of the contribution of tax on Nigeria’s economic development and its effects on companies’ performance in Nigeria, International Journal of Scientific and Research Publications, 7(7), 476–484.
International Monetary Fund (IMF). (2016). Economic Diversification in Oil Exporting Arab Countries [Annual Meeting]. Manama, Bahrain: International Monetary Fund. https://www.imf.org/en/Publications/Policy-Papers/Issues/2016/12/31/Economic-Diversification-in-Oil-Exporting-Arab-Countries-PP5038
Elechi, J. S., Kasie, E. G., & Chijindu, A. A. (2016). The contribution of the Nigerian Banks to the promotion of non-oil exports (1990–2013). Asian Journal of Economics, Business, and Accounting, 1(1), 1–13.
Miftahuddin, M., Mohammed, N., Abu Bakar, M., Shima, N., Syamsu, F., & Setiawan, I. (2021). Prediction of rainfall using ARIMA mixed models. Applied Mathematics and Computational Intelligence, 10(1), 101-126.
Maharani, A. B., Mohammed, N., Aleng, N. A., Larasati, N., Danong, A. P., Faawwaz, M. A. Y., Yanuar, Z. (2021). Modelling local-design strictness for COVID-19 pandemic in ASEA countries by using hybrid ARIMA-SVR and hybrid SEIR-ANN. Arab Journal of Basic and Applied Sciences, 28(1), 204-224.
Aleng, N. A., Naing, N. N., Mohammed, N., Abu Bakar, M. (2020). An alternative algorithm weighted MM-estimation for detection of outliers, JP Journal of Biostatistics, 17(2), 559-568.
Sagir, A. M., & Sathasivam, S. (2017). The use of artificial neural network and multiple linear regressions for stock market forecasting. MATEMATIKA, 33(1), 1–10.
Sagir, A. M. (2019). Prediction of the future effect of Nigeria economy growth on import and export commodities. Journal of Science, Technology, Mathematics and Education, 15(4), 73-84.
Sagir, A. M. (2018). Price prediction of a 5kg refill cooking gas using ANFIS. Katsina Journal of Natural and Applied Sciences. 4(1), 134-145.
Mythili, T., Mukherji, D., Padalia, N., & Abhiram, N. (2013). A heart disease prediction model using SVM-DecisionTrees-Logistic Regression (SDL). International Journal of Computer Applications, 68(16), 11-15.
Yaakob, S. B., Tahar, S. H. M., & Ahmed, A. (2018). Investment planning problem in power system using artificial neural network. Applied Mathematics and Computational Intelligence, 7(1), 13-22.
Najdi, N. F. N. & Ahad, N. A. (2019). Modification of ANOVA with various types of means. Applied Mathematics and Computational Intelligence, 8(1), 77-88.
Olayemi, F. O., & Akinwunmi, A. (2020). Comprehensive analysis of the effect oil and non-oil revenues on economic development in Nigeria. International Journal of Accounting Research, 5(3), 93-106.
Egbunike, F. C., Emudainohwo, B. C. & Gunardi, A. (2018). Tax revenue and economic growth. Signifikan Journal, 7(2).
Olayungbo, D. O., & Olayemi, O. F. (2018). Dynamic relationships among non-oil revenue, government spending, and economic growth in an oil producing country: Evidence from Nigeria. Future Business Journal, 4(2), 246-260, https://doi.org/10.1016/j.fbj.2018.07.002.
Victor, O., & Olaopa, O. (2009). Understanding the Niger delta conflict: Matters arising. In V. Ojakorotu (Ed.), Contending issues of the niger delta crises of Nigeria (pp. 1-19). JAPSS Press.
UNDP (United Nations Development Programme). (2006). Human Development Report: Niger Delta Human Development Report. New York. http://hdr.undp.org/sites/default/files/nigeria_hdr_report.pdf
Olasupo, O. (2013). The consequences of militancy in Nigeria’s Niger delta. JORIND, 11(2), 149 – 157.
Baldwin, R., & di Mauro, B. M. (2020). Mitigating the COVID economic crisis: Act fast and do whatever it takes. CEPR Press. https://voxeu.org/content/mitigating-covid-economic-crisisact-fast-and-do-whatever-it-takes
Onyekwena, C., & Ekeruche, M. A. (2020). Understanding the impact of the COVID-19 outbreak on the Nigerian economy. Brookings. https://www.brookings.edu/blog/africa-infocus/2020/04/08/understanding-the-impact-of-the-covid-19-outbreak-on-the-nigerianeconomy/#:~:text=The%20public%20budget%20increased%20from,been%20revised%20downwards%20from%2030
National Bureau of Statistics of Nigeria (2022). Non-Oil Income Tax (Corporate Income Tax) (2015-2020). https://nigerianstat.gov.ng/elibrary
Shakil, M. (2008). A multiple linear regression model to predict the student’s final grade in a mathematics class. Polygon, II. Multi-Disciplinary Publication of Miami Dade College, Hialeah Campus.
Shakil, M. (2009). A multiple linear regression model to predict the student’s final grade in a mathematics class. Bioresource Technology, 101(13), 4971-4979.
Angela, C., Maria, P., Belen, M., & Angel, C. (2013). Predicting academic performance and attrition in undergraduate students. Liberabit, 19(1), 101-112.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Mathematical Sciences and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
