Annual forecasting of inflation rate in Iran: Autoregressive integrated moving average modeling approach

Box‐Jenkins methodology is one of the most famous modeling approaches to describe the underlying stochastic structure and forecasting future values of various phenomena. In this methodology, the models are of type ARIMA, that is, autoregressive integrated moving average. Some advantages of those include robustness, easiness to use, and wide applicability in various disciplines ranging from engineering to economics. Inflation has been a highly discussed issue in economics. This research focuses on modeling and forecasting the yearly inflation rate of Iran from 1960 to 2019 using ARIMA. According to various measures, different ARIMA models are investigated to confirm their effectiveness. It is here showed that non‐seasonal ARIMA (1,0,0) is the most appropriate model for this application. In Box‐Jenkins methodology, time series models are in fact autoregressive integrated moving average models, also known as ARIMA models in statistics. Using various metrics, different types of models have been studied to confirm their effectiveness. Finally, non‐seasonal ARIMA (1,0,0) is indicated to be the most appropriate model to forecast the yearly inflation rate.