2017, Vol. 2, Issue 6, Part A
Time series modelling with application to Kenya’s inflation data comparison of ARIMA and ARCH modelsAuthor(s):
Catherine Nyaboke, George Otieno Orwa and Mungatu JosephAbstract:
Throughout the world, most central bank policy initiatives have been aimed at achieving and maintaining price stability and the Central Bank of Kenya is no exception to this rule. This study attempts to find the best model that can be used to forecast inflation by comparing the ARIMA and ARCH models. The main focus of the study is compare the forecast performance of ARIMA and GARCH models in order to find the best fit model that can be used to model and forecast Kenya’s monthly inflation rates for the inﬂation data spanning from January 2005 to June 2017.This study used the Box-Jenkins methodology and GARCH approach in analysing the inflation rates data. The best model for ARIMA and GARCH were selected based on model selection criteria AIC, AICc and BIC. The one with the least AIC and BIC was selected as the best model. A comparison was then made between ARIMA (1, 1, 12) and GARCH (1, 1) models in order to determine which better to use in similar situation. The accuracy of GARCH and ARIMA models was compared using diﬀerent statistical forecast evaluation criteria MAE, MSE, and MAPE eﬃciency. Results proved that the concluded that the forecast performance from GARCH (1, 1) model was greater than that from ARIMA (1, 1, 12) model. It was concluded that the ARIMA (1, 1, 12) model performs better than GARCH (1, 1) thus the ARIMA (1, 1, 12) is a better forecast model for inflation rate. The analysis of this study is carried out with the assist of R software. Presentation and explanations of results were aided by the use of graphs and tables.Pages: 16-22 | Views: 1045 | Downloads: 38Download Full Article: Click Here
How to cite this article:
Catherine Nyaboke, George Otieno Orwa, Mungatu Joseph. Time series modelling with application to Kenya’s inflation data comparison of ARIMA and ARCH models. Int J Stat Appl Math 2017;2(6):16-22.