Time Series Analysis and Forecasting of Household Products’ Prices (A Case Study of Nyeri County)

Abstract

Inflation has a significant impact on both consumable and non-consumable products and plays a critical role in determining the cost of living. The study aimed to investigate the trend of household consumable and non-consumable prices over the past three years and identify the best ARIMA model for future price predictions. The results showed that consumable goods played a greater role in determining the national inflation compared to non-consumable goods. A relationship was found between the changes in local-level prices and national monthly inflation rates, with consumable goods being fitted to an ARIMA (1,2,2) model and national inflation rates to ARIMA (3,1,0). Non-consumable goods were found to be a white noise. The models were found to be adequate in forecasting changes in prices, with their validity confirmed by the Box-Ljung test and autocorrelation coefficients of model residuals. This study demonstrated the importance of analyzing changes in products’ prices at a local level and how it affects the national inflation rate. In future, similar studies can be carried out in different counties and with a more comprehensive model to investigate the impact of the COVID-19 pandemic on the prices of household consumable and non-consumable goods at the local level.