Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models
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Date
2021-12-01
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Publisher
EJ-MATH, European Journal of Mathematics and Statistics
Abstract
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.
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Keywords
Consumer price index, forecasting, modelling. monetary policy, , SARIMA models, price stability.