Author(s)

Stanley Jere^*, Mubita Siyanga

Department of Mathematics and Statistics, Mulungushi University, Kabwe, Zambia.

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

Inflation, Holt’s Exponential Smoothing, Forecasting, Consumer Price Index, Mean Square Error and Mean Absolute Percentage Error

Jere, S. and Siyanga, M. (2016) Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing. Open Journal of Statistics, 6, 363-372. doi: 10.4236/ojs.2016.62031.