Forecasting Yearly Inflation Rate of Iran Using Box-Jenkins Models

Box-Jenkins prediction model is one of the most famous time series models and is important in predicting different economic phenomena. In Box-Jenkins methodology, time series models are in fact autoregressive integrated moving average models that are known as ARIMA models in statistics. Various models such as simple and multivariate regression, autoregressive, moving average, seasonal models and even unknown models can be derived from ARIMA models. In this research, the yearly inflation rate of Iran from 1960 to 2017 is modeled. Using various measures, different types of models have been studied to confirm their usefulness. Consequently, non-seasonal ARIMA (1,0,0) is suggested to be most appropriate.

Keywords:

Time Series , ARIMA , Box-Jenkins procedure , Inflation rate