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

Hossini, Seyyed Safdar; Eskandari Pour, Ali

Document Type : Research Paper

Authors

¹ Professor of Agricultural economics, Department of Agricultural Economics and Development, Tehran University.

² PhD student of Agricultural economics, Department of Agricultural Economics and Development, Tehran University

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

References

Arnade, C. and D. Pich (1998), Seasonality and Unit Roots: the Demand for Fruits, Agricultural Economics.

Beaulieu, J.J. and Miron, J.A (1993), Seasonal Unit Roots in Aggregate US Data, Journal of Econometrics, 55.

Campbell, H.E (2004), Prices, Devices, People, or Rules: The Relative Effectiveness of Policy Instruments in Water Conservation. Rev. Pol. Res., 21

Canova, F. and B.E. Hansen (1995), Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability. Journal of Business and Economic Statistics, 13.

Enders, W (1995), Applied Econometric Time Series. John Wiley & Sons, INC. New York.

Eric, A (2010), Modeling and Forecasting Inflation Rates in Ghana: an Application of SARIMA Models, dissertation submitted to the School of Technology and Business Studies, Hogskolan Dalarna in Partial Fulfillment of the Requirement for the Award of Master of Science Degree in Applied Statistics.

Franses P.H (1991), Seasonality, Non-seasonality and the Forecasting of Monthly time Series, International Journal of Forecasting.

Franses, P.H. and B. Hobijn (1997), Critical Value for Unit Root Tests in Seasonal Time Series, Journal of Applied Statistics, 24.

Griffin, R. and R. Sickles (2001), Demand Specification for Municipal Water Management: Evaluation of the Stone-Geary Form. Land Economics, 77(3).

Halim, S. and Bisono, I.N (2008), Automatic Seasonal Autoregressive Moving Average Models and Unit Root Test Detection. International Journal of Management Science and Engineering Management, 3(4).

Hamilton, J.D (1994), Time Series Analysis, Princeton Univ, Press, Princeton New Jersey.

Hylleberg, S., Engle, R., Granger, C.W.J. and B.S. Yoo (1990), Seasonal Integration and Cointegration, Journal of Econometrics, 44.

Kaïka, M (2003), The Water Framework Directive: a new Directive for a Changing Social, Political and Economic European Framework, European Planning Studies, 11(3).

Kihoro, J.M., Otieno, R.O., and C. Wafula (2004), Seasonal Time Series Forecasting: A Comparative Study of ARIMA and ANN Models, African Journal of Science and Technology, 5(2), 41-49.

Kirchgässner, G. and J. Wolters (2007), Introduction to Modern Time Series Analysis. Springer-Verlag Berlin Heidelberg.

Kleiber, C. and Zeileis, A (2008), Applied Econometrics with R. Springer Science Business Media, LLC, NY, USA.

Jain A., Varshney A.K., and U.C., Joshi (2001), Short-term Water Demand Forecast Modeling at IIT Kanpur using Artificial Neural Networks. Water Resources Management, 15.

Maniatis, P (2009), Forecasting Brussels Airport Passengers: Comparison between SARIMA and Exponential Smoothing Forecasting Techniques. The Business Review, 13(1).

Pearce, D. (1999), Pricing Water: Conceptual and Theoretical Issues, Paper for European Commission for the Conference on Pricing Water: Economics, Environment and Society. Portugal: Sintra.

Pfaff, B. (2008), Analysis of Integrated and Cointegrated Time Series with R. 2Ed, Springer Science Business Media, LLC, NY, USA.

Renwick, M. and S. Archibald, (1998), Demand Side Management Policies for Residential Water Use: Who Bears the Conservation Burden? Land Economics, 74(3).

Shumway, R.H. and D.S. Stoffer, (2006), Time Series Analysis and Its Applications with R Examples, 2Ed, Springer Science Business Media, LLC, NY, USA.

Taylor, A.M.R. (1997), On the Practical Problems of Computing Seasonal Unit Root Tests. International Journal of Forecasting, 13.

Tietenberg T (1996), Environmental and Natural Resources. Harper Collins. N.

Economics Research

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

References

References

Volume 12, Issue 46 - Serial Number 3
October 2012
Pages 85-100

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

References

References

Volume 12, Issue 46 - Serial Number 3October 2012Pages 85-100

Volume 12, Issue 46 - Serial Number 3
October 2012
Pages 85-100