Forecasting Inflation Rate in Ghana using Seasonal Autoregressive Integrated Moving Average Model with Monthly Consumer Price Index, 2012 -2022

Abstract

This study aimed at modeling and forecasting the inflation rate in Ghana using a seasonal autoregressive integrated moving average model with monthly consumer price index data from January 2012 to December 2022. Using the Philip-Parron unit root test, the result showed that the time series data was stationary in its first difference, showing that the consumer price index was integrated with the first order. Also, from the seasonal graph, seasonality was observed in the data. The correlogram of ACF and PACF helped to select the appropriate lag for p and q. Box-Jenkins procedure was applied to identify the appropriate model that fit the data. From the Box-Jenkins procedure, the SARIMA(1,1, 1)(1,1,1)12 model was identified as the best model to forecast the inflation rate. From the forecast graph, inflation will begin to rise in the second quarter of 2023. However, the forecast from January to March,, 2023 inflation rates were 54.9, 56.5 and 50.2, respectively. Therefore, it is highly likely that Ghanaian inflation will be rising in the subsequent months based on the 2012 to 2022 Consumer Price Index. The appropriate authorities should put monetary and fiscal policy measures in place to moderate the envisaged rise in inflation.