FORECASTING OF NIGERIA MANUFACTURING SECTOR GROWTH RATES USING ARIMA MODEL

  • Oniore O. Jonathan Faculty of Humanities, Social and Management Sciences, Bingham University, Karu, Nigeria
  • Ezenekwe R. Uju Faculty of Social Sciences, Nnamdi Azikiwe University, Awka, Nigeria
  • Akatugba D. Oghenebrume Faculty of Social Sciences, Niger Delta University, Wilberforce Island, Bayelsa, Nigeria
Keywords: Economic Growth; Manufacturing; Forecasting; ARIMA; Nigeria

Abstract

The objective of this study is to identify a forecasting model that can predict Nigeria's future manufacturing sector growthrateand to ascertain whether policy makers could maintain a steady and sustainable growth rate in the manufacturing sector.The study employed Autoregressive Integrated Moving Average (ARIMA) on annual data from 1970 to 2014 on manufacturing production index (MPI) as a measure of manufacturing sector growth rate. The ARIMA model selected is the Autoregressive (AR) [ARIMA (1, 0, 0)]. That is, the AR1) model was selected as the most appropriate for forecasting model for manufacturing sector growth rates in Nigeria. The ARIMA model was selected on the basis of Autocorrelation (AC) and Partial Autocorrelation (PAC) Function and the inverse root of AR/MA polynomials for stability of the estimated model The forecasted values of manufacturing sector growth for 2015, 2016, 2017, 2018, 2019 and 2020 using Dynamic Forecast were

72.1%, 75.6%, 75.9%, 76.4%, 76.8% and 77.2% respectively. The major finding of this study is that Nigeria's manufacturing sector future growth rate is moving gradually with an average annual projected growth of approximately 90.8 %. The projected rate showed that Nigeria government needs to double her efforts in order to fructify its vision of becoming twenty largest economies in the world by 2020 and the 12th largest economy by 2050.

Author Biographies

Oniore O. Jonathan , Faculty of Humanities, Social and Management Sciences, Bingham University, Karu, Nigeria

Department of Economics

Ezenekwe R. Uju , Faculty of Social Sciences, Nnamdi Azikiwe University, Awka, Nigeria

Department of Economics

Akatugba D. Oghenebrume, Faculty of Social Sciences, Niger Delta University, Wilberforce Island, Bayelsa, Nigeria

Department of Economics

References

Abeysinghe, T. (1998). Forecasting Singapore's quarterly GDP with monthly external trade. Int. J. Forecasting, 14:505-513
Adebiyi, M. A., Adenuga, A.O., Abeng, M.O., Omanukwe, P.N., & Ononugbo, M.C. (2010). Inflation forecasting models for Nigeria. Central Bank of Nigeria Occasional Paper No. 36. Abuja: Research and Statistics Department.
Assis, K, Amran, A; & Remali, Y. (2010). Forecasting cocoa beans pricis using univariate time series models. Int. Refereed Res, J. 1:71-80.
Baffigi, A; Golinelli, R; & Parigi, G. (2004). Bridge models forecast the Euro area GDP. Int. J. Forecasting, 20:447-460.
Bourbonnais, R. (2004). Manuel et excercises corrigés, 4e edition Dunod, Paris
Box, G., & Jenkins, G. (1976). Time series analysis: Forecasting and control (1st Edition). , San Fransisco: Holden-Day.
Brooks, C. (2008). Introductory Econometrics for Finance (2nd edition). England: Cambridge University Press.
Central Bank of Nigeria (2009). 50 Years of Central Banking in Nigeria, 1958-2008 Egbon, P.C. (1995). Industrial policy and manufacturing performance in Nigeria.
NCEMA, Monograph Series No, 7.
El-Mefleh, M. A., & Shotar, M. (2008). A contribution to the analysis of the economic growth of Qatar. Applied Econometrics and International Development Journal, 8 (1), 18-35.
ERGP (2017). Economic recovery and growth Plan 2017-2020. Ministry and Budget and National Planning, Abuja: Nigeria.
Floros, C. (2005). Forecasting the UK unemployment Rate: Model comparisons. International Journal of Applied Econometrics and Quantitative Studies, 2- 4:55-72.
Gan, W; & Wong, F. (1993). ABayesian vector autoregression model for forecasting quarterly GDP: The Singapore experience. Singapore Economic Rev, 38:15- 34.
Gil-Alana, A. (2001). A fractionally integrated exponential model for UK unemployment. J. Forecasting, 20:329-340.
Golan, A; & Perloff, J. (2002). Superior forecasts of the U.S. unemployment rate using a Nonparametric Method. UDARE Working Papers, P 956. http://repositories.cdlib.org/are ucb/956
Greene, W. (2003). Econometric Analysis.3th ed, Englewood Cliffs, N.J.: Prentice Hall.
Gujarati D (2004). Essentials of Econometrics. 4th ed., McGraw-Hill,New York. Gujarati, D. (2009). Essentials of Econometrics (4th ed.). New York: McGraw-Hill. Hanke, J. E., & Wichern, D.W. (2005). Business Forecasting (8th ed.). New York:
Pearson Educational Incorporated.
Javedani, H; & Suhartono, L. (2010). An evaluation of some classical methods for forecasting electricity usage on specific problem. Proceedings of the Regional Conference on Statistical Sciences (RCSS'10), 47-56.
Kamil, A; & Noor, A. (2006). Time series modeling of Malaysian raw palm oil price: Autoregressive conditional heteroskedasticity (ARCH) model approach. Discov. Math, 28:19-32.
Katimon, A; & Demun, A. S. (2004). Water use at Universiti Teknologi Malaysia: application of ARIMA model. Jurnal Teknologi, 41 (B), 47-56.
Nkwatoh, L. (2012). Forecasting unemployment rates in Nigeria using univariate time series models. Int. J. Bus. Commun, 1(12):33-46.
Purna, P. (2012). Use of univariate time series models for forecasting cement productions in India. Int. Res. J. Financ. Econ, 83:167-179.
Roberts, F.S. (2006). Discrete mathematical models with application to social, biological and environmental problems. New York: Prentice Hall.
Salau, M.O. (1998). ARIMA modelling of Nigeria's crude oil exports, AMSE.
Modelling, Measurement & Control, 18 (1), 1-20.
Samad, Q. A; Ali, M. Z., & Hossain, M. Z. (2002). The forecasting performance of the Box-Jenkins Model: the case of wheat and wheat flour prices in Bangladesh. The Indian Journal of Economics, LXXXII (327), 509-518.
Sarbijan, S. (2014). A Comparison study between Markov Switching time series model and Arima model for forecasting Iran economic growth. Int. J. Sci. Res. Knowledge, 2:058-065.
Syariza, A; Noorhafiza, M. (2005). Comparison of time Series methods for electricity forecasting: A case study in Perlis. ICOQSIA, 6-8 December, Penang, Malaysia.
Taylor, W. (2008). A comparison of univariate time series methods for forecasting intraday arrivals at a call center. Manage. Sci. 54:253-265.
Valle, H. A. S. (2002). Inflation forecasts with ARIMA and vector autoregressive models in Guatemala. Economic Research Department, Guatemala: Banco de Guatemala.
Zhou, B; He, D; & Sun, Z. (2006). Modeling and simulation tools for emerging telecommunication networks. Springer US, 101-121.
Published
2019-06-26