Forecasting Inflation based on Stochastic Differential Equations and Alternative Models
(A Comparative Study)

Molabahrami, Ahmad; Khodavaisi, Hassan; Hossaini, Reza

All

Webpages

Books

Journals

Tarbiat Modares University Press

The Economic Research

Volume 13, Issue 1 (2013) QJER 2013, 13(1): 25-46 | Back to browse issues page

‎ 20.1001.1.17356768.1392.13.1.8.0

Mendeley

Zotero

RefWorks

Molabahrami A, Khodavaisi H, Hossaini R. Forecasting Inflation based on Stochastic Differential Equations and Alternative Models (A Comparative Study). QJER 2013; 13 (1) :25-46
URL: http://ecor.modares.ac.ir/article-18-9570-en.html

Forecasting Inflation based on Stochastic Differential Equations and Alternative Models (A Comparative Study)

Ahmad Molabahrami¹

, Hassan Khodavaisi ^*

², Reza Hossaini³

1- M.A. in Economics, Urmia University
2- Assistant Professor of Economics, Urmia University
3- Ph.D. in Economics, Member of Research Committee, Agriculture Bank

Abstract: (9400 Views)

In this paper, it is tried to propose a robust model for predicting inflation in Iran among alternative models. For doing this, monthly data from April 1990 to the end of September 2009 is used. Firstly, it is tried to determine whether the CPI data is chaotic or stochastic. It is shown that it is chaotic rather than stochastic. Therefore, it is predictable. Then, a stochastic differential equation model is estimated (specifically a geometric Brownian motion) for CPI in Iran. In order to compare the prediction power of the model other alternative models of prediction like ARMA, non-linear GARCH, EGARCH, TGARCH are also used to extrapolate inflation during a six month prediction period. Based on RMSE, MAE, U-Tail, it is revealed that stochastic differential equation model is much more robust than the alternative models mentioned above.

Keywords: E47, Chaos tests, Inflation prediction, Stochastic differential equations, Neural network, Time series models JEL Classification: E37

Full-Text [PDF 267 kb] (3495 Downloads)

Received: 2011/03/15 | Accepted: 2012/02/26 | Published: 2013/04/21

Rights and permissions
	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.