Modeling Consumer Price Index in Zambia: A Comparative Study between Multicointegration and Arima Approach

Stanley Jere,Alick Banda,Edwin Moyo,Rodgers Chilyabanyama

doi:10.4236/ojs.2019.92018

Stanley Jere, Alick Banda + Show 2 more

Open Access

https://doi.org/10.4236/ojs.2019.92018

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Abstract

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.

Highlights

Rising prices affect everyone in terms of purchasing power especially if wages remain constant
Autoregressive Integrated Moving average (ARIMA) (3, 1, 3) model was chosen from other ARIMA models as it exhibited the smallest Mean Error (ME), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Squared Error (MASE)
Multicointegration was identified as the more accurate model for forecasting compared to the ARIMA (3, 1, 3)

Summary

Introduction

Rising prices affect everyone in terms of purchasing power especially if wages remain constant. The consumer price measures the weighted average of prices of a basket of goods and services, which include fuel, transport, food and medical care purchased by households. CPI identifies price changes across product categories re-. The CPI may not adequately explain actual movements in the costs of living according to [2]. This may be as a result of some biases which may include inaccurate data. The Engel curve method introduced by [3] addresses the above bias

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