Abstract
In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. The mobile cellular subscription data for the study were taken from the administrative data submitted to the Zambia Information and Communications Technology Authority (ZICTA) as quarterly returns by all three mobile network operators Airtel Zambia, MTN Zambia and Zamtel. The time series of annual figures for mobile cellular subscription for all mobile network operators is from 2000 to 2014 and has a total of 15 observations. Results show that the ARIMA (1, 2, 1) is an adequate model which best fits the mobile cellular subscription time series and is therefore suitable for forecasting subscription. The model predicts a gradual rise in mobile cellular subscription in the next 5 years, culminating to about 9.0% cumulative increase in 2019.
Highlights
In Zambia, the penetration of information and communication technology (ICT) in general and mobile in plays an important role in compilation of the national Gross Domestic Product (GDP)
The mobile cellular subscription (MCS) data for the study has been taken from the administrative data submitted to the Zambia Information and Communications Technology Authority (ZICTA) as quarterly returns by all three mobile network operators (MNOs)
An Auto Regressive Integrated Moving Average (ARIMA) model with least measures of accuracy the Akaike Information Criterion (AIC) and s Bayesian Information Criterion (SBC) is considered an efficient model for prediction
Summary
In Zambia, the penetration of information and communication technology (ICT) in general and mobile in plays an important role in compilation of the national Gross Domestic Product (GDP). Studies have shown that diffusion of mobile telecommunication affects the growth of GDP. How to cite this paper: Siluyele, I. and Jere, S. (2016) Using Box-Jenkins Models to Forecast Mobile Cellular Subscription. Time series modelling is an important part of every field. It provides both short and long term forecasting techniques. Literature shows that researchers have used both stochastic and deterministic models to model and forecast telecommunication data. Stochastic models attributed to Box-Jenkins, the Auto Regressive Integrated Moving Average (ARIMA) models have been found to be more efficient and reliable even for short term forecasting than the deterministic models. Stochastic models are distribution-free as no assumptions are required about the data or parameter the adoption of the forecasting methodology in this paper
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