Price instability has been a major concern in most economies. Kenya's commodity markets have been characterized by high price volatility affecting investment and consumer behaviour due to uncertainty on future prices. Therefore, precise forecasting models can help consumers plan for their expenditure and government policymakers formulate price control measures. Due to the seasonality of Kenya's food and beverage price indices, the current study postulates that the Seasonal Autoregressive Integrated Moving Average (SARIMA) model can best be the best fit model for the data. The study used secondary data on Kenya's monthly food and beverage prices index from January 1991 to February 2020 to examine the predictive ability of the possible SARIMA models based on the minimisation of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The cross-validation test results of 6, 12, 18, 24, 30, and 36 step-ahead forecasts demonstrated that SARIMA models are unstable for use in forecasting over a long-time period with a tendency of increasing prediction errors with an increase in the forecast period. It is anticipated that the findings of the current study will provide necessary valuable information to the policymakers and stakeholders to understand future trends in commodity price

Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.