A Time Series Analysis of Federal Budgetary Allocations to Education Sector in Nigeria (1970-2018)

N. M. Eze^1, , O. C. Asogwa², A. O. Obetta¹, K. C. Ojide² and C. I. Okonkwo²

¹Department of Statistics, University of Nigeria, Nsukka

²Department of Mathematics, Computer Science, Statistics and Informatics, Alex Ekwueme Federal University Ndufu-Alike Ikwo

Cite this paper:
N. M. Eze, O. C. Asogwa, A. O. Obetta, K. C. Ojide and C. I. Okonkwo. A Time Series Analysis of Federal Budgetary Allocations to Education Sector in Nigeria (1970-2018). American Journal of Applied Mathematics and Statistics. 2020; 8(1):1-8. doi: 10.12691/ajams-8-1-1

Abstract

This work is carried out to statistically analyze federal budgetary allocations to the education sector in Nigeria (1970-2018). Time series analysis is used to analyse the data using the Box and Jenkins modeling approach. This involved identification of model, estimation of parameters, diagnostic checking of the model and forecasting. ARIMA (1, 1, 1) was identified in the course of identification of the model. ARIMA (1, 1, 0) was selected as a parsimonious model using the Bayesian Information Criterion (BIC). Also, the diagnostic check was carried out and it was found that the model is adequate. Forecasts are equally made for the year 2019, 2020, 2021 and 2022 using the model obtained. It was observed that there is an upward trend from 2019-2022 forecasts, hence using the model will bring about increment in the future budgets.

Keywords:
budget box-jenkins methodology Auto Regressive integrated Moving Average (ARIMA) Beyesian Information Criterion (BIC) forecasting

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References:

[1]	In. Microsoft Encarta Dictionary (2009 ed.). Redmond, USA.
[2]	Adegbite, J. G. (2007). The education reform agenda challenges for tertiary education administration in Nigeria paper presented at the 6th Annual seminar of the conference of Registrants of Colleges of Education in Nigeria.
[3]	Oyaneye, O. Y. (2006). Current issues in the administration of university education in Nigeria; Lecture delivered on the 15th Convocation Ceremony of University of Ado-Ekiti, Ado Ekiti, Nigeria 2006.
[4]	Bamiro, O. A & Adedeji, O. S. (2010). Sustainable financing of higher education in Nigeria, Ibadan: Ibadan University Press.
[5]	Agha, N. C. (2014). Funding university education in Nigeria: Implications on institutional performance. International Journal of scientific research and Management, 2(8), 1145-1153.
[6]	Gupta, S., Verhoeven, M., & Tiongson, E. R. (2002). The effectiveness of government spending on education and health care in developing and transition economies. European Journal of Political Economy, 18(4), 717-737.
[7]	Omotor, D. G. (2004). An analysis of federal government expenditure in the education sector of nigeria: implications for national development. Journal of Social Sciences,9(2),105-110.
[8]	Durosar, D. O. & Fasasi Y. A. (2009). Education finance in contemporary school administration in Nigeria/K. Oyesi Kw, S. Ogunsaju and J. Oni (Eds). 109-119 Ijagun, Ijebu.
[9]	Dauda, R. O. (2010). Role of Human Capital in Economic Development: An Empirical Study of Nigerian Case. In Manuscript, Oxford Business and Economics Conference Program.
[10]	Ude, D., & Ekesiobi, S. (2014). Panel investigation of states social spending and social outcome: perspective of education in Nigeria. International Journal of Economics, Finance and Management 3 (5), 244-255.
[11]	Ojewumi, J. S., & Oladimeji, W. O. (2016). Effect of public spending on the growth of educational sector in nigeria. JORIND 14(2). Retrieved from www.transcampus.org/journal.
[12]	Central Bank of Nigeria (2018). Statistical Bulletin and information. Abuja Nigeria: central Bank of Nigeria. Federal government of Nigeria (2018). Nigeria Education sector Diagnosis. Abuja Nigeria: Federal government Press.
[13]	Box, George & Jenkins, Gwilym (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.
[14]	Vandaele, W. (1983). Applied Time Series and Box-Jenkins Models. Orlando: Academic Press.
[15]	Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. JASA, 74 No. 366, p. 427-431.
[16]	Zhihua, W., Yongbo, Z., & Huimin, F. (2014). Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size.
[17]	De Gooijer, J., Abraham, B., Gould, A., & Robinson, L. (1985). Methods for Determining the Order of an Autoregressive-Moving Average Process: A Survey. International Statistical Review/Revue Internationale De Statistique, 53(3), 301-329.
[18]	Hillmer, S. C. & Tia, G. C. (1982). An ARIMA-model-based approach to seasonal adjustment, JASA, 77, 63-70.
[19]	Tsay, R. S., & Tiao, G. C. (1983). Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models. Technical Report #2, Statistics Research Center, Graduate School of Business, University of Chicago, Chicago.
[20]	Abraham, B., & Vijayan, K. (1988). A statistic to check model adequacy in time series. Comm. Statist. Theory Methods. 17(12), 4271-4278.
[21]	Hokstad, P. (1983). A method for diagnostic checking of time series models. J. Time Ser. Anal. 4(3), 177-183.
[22]	Anderson, O. D. (1975). Time Series Analysis and Forecasting. London: Butterworth & Co.
[23]	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, 2nd ed. San Francisco: Holden Day.
[24]	Granger & Newbold (1977). Forecasting Economic Time Series. Academic Press.