Abstract:
This study attempts to use a univariate model in the form of Autoregressive Integrated Moving Average model (ARIMA) developed by Box and Jenkins to forecast inflation for Kenya. STATA statistical package and R software will be employed in performing the time series analysis which will be exploited to check data validation, Augmented Dickley-Fuller Test (ADF TEST) will be used in testing stationarity of the time series data, estimation and order selection, parameter estimation, model diagnostic checking and forecasting. This paper will use changes in monthly consumer price index obtained from the National Bureau of Statistics to predict movements in the general price level. Based on different diagnostic and evaluation criteria, the best forecasting model for predicting inflation in Kenya will be identified. The results will enable policy makers and businesses to track the performance and stability of key macroeconomic indicators using the forecasted inflation.
