Modelling and forecasting using time series Garch models: an application of Tanzania inflation rate data.

The research study is based on financial time series modelling with special application to modelling inflation data for Tanzania. In particular the theory of univariate non linear time series analysis is explored and applied to the inflation data spanning from January 1997 to December 2010. The data was obtained from Tanzania National Bureau of Statistics. Time series models namely, the autoregressive conditional heteroscedastic (ARCH) (with their extensions to the generalized ARCH (GARCH)) models were fitted to the data. The stages in the model building namely, identification, estimation and checking has been explored and applied to the data. A best fitting model was selected based on how well the model captures the stochastic variation in the data (goodness of fit). The goodness of fit is assessed through the Akaike information criteria (AIC) , Bayesian information criteria (BIC) and standard error (SE) Based on minimum and BiC values, the best fit GARCH models tend to be GARCH(1, 1) and GARCH(1 , 2). After estimation of the parameters of selected models, a series of diagnostic and forecast accuracy test were performed. Having satisfied with all the model assumptions, GARTCH (1, 1) model was judged to be the best model for forecasting. Based on the selected model, we forecasted twelve (12) months inflation rates of Tanzania in-sample period (that is from January 2010 to De- cember 2010). From the results, it has been observed that the forecasted series are close to the actual series.