The Contribution of Econometric Seasonal Models to Forecasting CPI in the City of Tehran

Abstract

Since many years ago, Iran has faced a macroeconomic problem of inflation. The problem of high inflation rate caused the economic growth of this country to slow down. As we all know, inflation is one of the major economic challenges that most of the countries in the world are facing, especially those in Asia including Iran. Therefore, forecasting the variable of inflation rate in Iranian economy becomes very important for the government to design effective economic strategies or monetary policies to combat any unexpected high inflation in this country. This paper studies a model of seasonal autoregressive integrated moving average to forecast inflation rates in the city of Tehran. Using monthly inflation data from March 2002 to February 2010 in Tehran, we find that ARIMA models can explain the behavior of actual data of inflation rate in Tehran in an acceptable way. Based on the selected model, we forecast the value of monthly inflation rate for six (6) months ahead in the city of Tehran that are out of the sample period (i.e. from March 2010 to August 2010). The observed inflation rate from March 2010 to August 2010 which was published by Tehran Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point inflation rate in Tehran August 2010.

Keywords

• seasonal models
• monthly unit root
• sarima model
• price index
• tehran