2022, Vol. 7, Issue 4, Part B
Predicting access to electricity in Nigeria: Autoregressive integrated moving average approachAuthor(s):
Seun Adebanjo and Pius SibeateAbstract:
Having Access to affordable, reliable and sustainable electricity is a vital instrument of energy security that will develop the private businesses, industries and good livelihood of well-meaning Nigerians.
The sole objective of this paper is to adopt the autoregressive integrated moving average (ARIMA) model to predict Access to Electricity in Nigeria.
The unit root test was carried out and the result revealed that Access to Electricity becomes stationary after the first difference this suggests that further time series analysis like the ARIMA model can be performed.
The ARIMA approach was adopted and different tentative ARIMA models were obtained. The ARIMA (1, 1, 6) is the best selected ARIMA model among the other tentative models because it has the highest no of significant coefficients, highest adjusted R-squared, lowest volatility, and lowest AIC and SBIC. The forecasted values from 2021 to 2030 by the fitted ARIMA (1, 1, 6) model provided a future view of the possibility of having better Access to Electricity in Nigeria and this is also a significant contribution to the existing body of knowledge as there is no previous study that has applied ARIMA model to Access to Electricity in Nigeria which is one of the major challenges confronting the country. As a result of this, the Nigeria government need to develop an efficient energy policy to meet up with the united nation development goal target that will ensure that all the developing nations including Nigeria will have access to affordable, reliable and sustainable energy security in terms of access to sustainable electricity that will drive the economic growth and development in Nigeria.Pages: 155-159 | Views: 228 | Downloads: 13Download Full Article: Click Here
How to cite this article:
Seun Adebanjo, Pius Sibeate. Predicting access to electricity in Nigeria: Autoregressive integrated moving average approach. Int J Stat Appl Math 2022;7(4):155-159.