%0 Journal Article
%T Modeling and Forecasting of Consumer Price Index of Foods and Non-Alcoholic Beverages in Kenya Using Autoregressive Integrated Moving Average Models
%A Michael Mbaria Chege
%J Open Journal of Statistics
%P 677-688
%@ 2161-7198
%D 2024
%I Scientific Research Publishing
%R 10.4236/ojs.2024.146030
%X Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.
%K Consumer Price Index
%K Food and Non-Alcoholic Beverages
%K Autoregressive Integrated Moving Averages
%K Modeling and Forecasting
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=137219