Prediction of Electricity Consumption in Ghana: Long or Short Memory

Abstract

In this study, we explore both univariate and multivariate aspects of time series analysis. In the univariate aspect, we evaluated the predictive performance of three widely used univariate time series methods in forecasting the electricity consumption in Ghana during the 1980 – 2011 periods. The three univariate time series approaches are autoregressive integrated moving average (ARIMA), autoregressive fractional integrated moving average (ARFIMA) and exponential smoothing. In each approach, we examined competing models and the “best” model according to the minimum information criterion and diagnostic checking was selected. The forecast accuracy measure (i.e.; mean absolute forecast error, MAFE) was computed for each “best” model in the three different approaches. The empirical results revealed that the MAFE for ARIMA, ARFIMA and exponential smoothing were 31.3%, 9.4% and 41.6% respectively. Thus, the comparative analysis of the forecast performance of these methods clearly concluded that the ARFIMA method gives better forecast in predicting electricity consumption in Ghana. And, in the multivariate aspect, we examined whether GDP, export, import and population influences electricity consumption. The results revealed a feedback causality between electricity consumption and economic growth. Again, we established that there exists an uni-directional influence of import, export, population towards electricity consumption. The “best” model of the univariate approach is ARFIMA (2,0.31,1) with MAFE of 9.4% while the “best” model for the multivariate approach is vector error correction model VECM (3) with MAFE of 1.5%. Thus, the multivariate approach has a better predictive performance in forecasting electricity consumption in Ghana. This shows how superior the multivariate approach against the univariate time series approach.