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.
References
[1]
Babarinde, S. and Ajayeoba, T. (2014) Food for Survival or Food for Healthful Liv-ing—An Overview of the Functionality of Food among the People of Nigeria. A Quarterly Publication of the Faculty of Science, Adeleke University, 1, 129-140.
[2]
Sikalidis, A.K. (2018) From Food for Survival to Food for Personalized Optimal Health: A Historical Perspective of How Food and Nutrition Gave Rise to Nutrigenomics. JournaloftheAmericanCollegeofNutrition, 38, 84-95. https://doi.org/10.1080/07315724.2018.1481797
[3]
Firth, J., Gangwisch, J.E., Borsini, A., Wootton, R.E. and Mayer, E.A. (2020) Food and Mood: How Do Diet and Nutrition Affect Mental Wellbeing? BMJ, 369, m2382. https://doi.org/10.1136/bmj.m2382
[4]
Bae, H., Kim, M. and Hong, S.M. (2008) Meal Skipping Children in Low-Income Families and Community Practice Implications. NutritionResearchandPractice, 2, 100-106. https://doi.org/10.4162/nrp.2008.2.2.100
[5]
KIPPRA (2020) Creating an Enabling Environment for Inclusive Growth in Kenya. The Kenya Institute for Public Policy Research and Analysis.
[6]
World Bank (2024) GDP Per Capita (Current US$)-Kenya. World Bank Open Data. https://data.worldbank.org
[7]
Diwakar, V. and Shepherd, A. (2018) Understanding Poverty in Kenya: A Multidimensional Analysis. Chronic Poverty Advisory Network.
[8]
Greenlees, J.S. (2008) Addressing Misconceptions about the Consumer Price Index. Monthly Labor Review.
[9]
Kuhe, D. and Egemba, R. (2016) Modeling and Forecasting CPI Inflation in Nigeria: Application of Autoregressive Integrated Moving Average Homoskedastic Model. Journal of Scientific and Engineering Research, 3, 57-66.
[10]
Ibrahim, A. and Olagunju, S.O. (2020)
[11]
Norbert, H. (2016) Modeling and Forecasting Consumer Price Index (Case of Rwanda). AmericanJournalofTheoreticalandAppliedStatistics, 5, 101-107. https://doi.org/10.11648/j.ajtas.20160503.14
[12]
Mia, M.S., Nabeen, A.H.M.M.R. and Akter, M.M. (2019) Modelling and Forecasting the Consumer Price Index in Bangladesh through Econometric Models. American Scientific Research Journal for Engineering, Technology, and Sciences, 59, Article 1.
[13]
Mwanga, Y. (2020) Arima Forecasting Model for Uganda’s Consumer Price Index. AmericanJournalofTheoreticalandAppliedStatistics, 9, 238-244. https://doi.org/10.11648/j.ajtas.20200905.17
[14]
Mohamed, J. (2020) Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. AmericanJournalofTheoreticalandAppliedStatistics, 9, 143-153. https://doi.org/10.11648/j.ajtas.20200904.18
[15]
International Monetary Fund (2023) Data Quality Management and Reporting. In: International Monetary Fund, Eds., Consumer Price Index Manual: Concepts and Methods, 2nd Edition, International Monetary Fund.
[16]
Jerven, M. (2016) Data and Statistics at the IMF: Quality Assurance for Low-Income Countries (Background Paper BP/16/06; IEO Background Paper). Independent Evaluation Office, International Monetary Fund.
[17]
Schaffer, A.L., Dobbins, T.A. and Pearson, S. (2021) Interrupted Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models: A Guide for Evaluating Large-Scale Health Interventions. BMCMedicalResearchMethodology, 21, Article No. 58. https://doi.org/10.1186/s12874-021-01235-8