Open AccessModeling the Behavior of Inflation Rate in Albania Using Time Series
Author(s) -
Rozana Liko
Publication year - 2017
Publication title -
journal of advances in mathematics
Language(s) - English
Resource type - Journals
ISSN - 2347-1921
DOI - 10.24297/jam.v13i3.6196
Subject(s) - heteroscedasticity , autoregressive conditional heteroskedasticity , mathematics , autoregressive model , autoregressive–moving average model , inflation (cosmology) , series (stratigraphy) , econometrics , moving average model , statistics , time series , star model , normality , autoregressive integrated moving average , volatility (finance) , biology , theoretical physics , paleontology , physics
In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation. Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).