Modelling and forecasting Ghana’s inflation rates using sarima models

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Time series analysis and forecasting is an efficient versatile tool in diverse applications such as in economics and finance, hydrology and environmental management fields just to mention a few. Among the most effective approaches for analysing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). In this study we used Box-Jenkins methodology to build ARIMA model for Ghana’s monthly inflation rate for the period 1990-2010 with a total of 252 data points. In this research, ARIMA (1, 1, 1) (0, 0, 1)12 model was developed, and written as. y ̂_t=1.7958y_(t-1) -0.7958y_(t-2 )-0.7446e_(t-12)+0.237453e_(t-13)-0.3189e_(t-1).This model is used to forecast Ghana’s monthly inflation for the upcoming year 2011.The forecasted results will help policy makers gain insight into more appropriate economic and monetary policy in other to combat the predicted rise in inflation rate beginning the second quarter of 2011.In light of this we recommend the assessment and the performance of ARIMA (1, 1, 1) (0, 0, 1)12 in forecasting Ghana’s inflation rate as a future research topic.
A Thesis submitted to the Department of Mathematics, Kwame Nkrumah University of Science and Technology in partial fulfilment of the requirements for the award of Master of Science in Industrial Mathematics