Open AccessAnnual forecasting of inflation rate in Iran: Autoregressive integrated moving average modeling approach
Author(s) -
JafarianNamin Samrad,
Fatemi Ghomi Seyyed Mohammad Taghi,
Shojaie Mohsen,
Shavvalpour Saeed
Publication year - 2021
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12344
Subject(s) - autoregressive integrated moving average , econometrics , inflation (cosmology) , autoregressive model , box–jenkins , moving average , robustness (evolution) , autoregressive–moving average model , computer science , time series , economics , statistics , mathematics , machine learning , biochemistry , physics , chemistry , theoretical physics , gene
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.