Author(s)

Stanley Jere^1*, Alick Banda¹, Rodgers Chilyabanyama¹, Edwin Moyo²

¹Department of Mathematics and Statistics, Mulungushi University, Kabwe, Zambia.
²Department of Mathematics and Statistics, Northrise University, Ndola, Zambia.

Consumer Price Index (CPI) is an important indicator used to determine inflation. The main objective of this research was to compare the forecasting ability of two time-series models using Zambia Monthly Consumer Price Index. We used monthly CPI data which were collected from January 2003 to December 2017. The models that were compared are the Autoregressive Integrated Moving average (ARIMA) model and Multicointegration (ECM) model. Results show that the ECM was the best fit model of CPI in Zambia since it showed smallest errors measures. Lastly, a forecast was done using the ECM and results show an average growth rate for food CPI at 6.63% and an average growth rate for nonfood CPI at 7.41%. Forecasting CPI is an important factor for any economy because it is essential in economic planning for the future. Hence, identifying a more accurate forecasting model is a major contribution to the development of Zambia.

Consumer Price Index, Multicointegration, ARIMA, ECM, Forecast

Jere, S. , Banda, A. , Chilyabanyama, R. and Moyo, E. (2019) Modeling Consumer Price Index in Zambia: A Comparative Study between Multicointegration and Arima Approach. Open Journal of Statistics, 9, 245-257. doi: 10.4236/ojs.2019.92018.