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Seoul Journal of Economics
[ Article ]
Seoul Journal of Economics - Vol. 22, No. 3, pp.289-310
ISSN: 1225-0279 (Print)
Print publication date 31 Aug 2009
Received 23 Jun 2008 Revised 08 May 2009

A Long Memory Model with Mixed Normal GARCH for US Inflation Data

Yin-Wong Cheung ; Sang-Kuck Chung
Professor, Department of Economics, E2, University of California, 1156 High Street, Santa Cruz, CA 95064, USA, Tel: +831-459-4247, Fax: +831-459-5077 cheung@ucsc.edu
Corresponding Author, Associate Professor, Department of Economics, Center for Research on Northeast Asian Economy, Inje University, Obang-dong 607, Kimhae, Kyungnam 621-749, Korea, Tel: +82-55-320-3124, Fax: +82-55-337-2902 tradcsk@inje.ac.kr

JEL Classification: C22, C51, C52, E31

Abstract

We introduce a time series model that captures both long memory and conditional heteroskedasticity and assess its ability to describe the US inflation data. Specifically, the model allows for long memory in the conditional mean formulation and uses a normal mixture GARCH process to characterize conditional heteroskedasticity. We find that the proposed model yields a good description of the salient features, including skewness and heteroskedasticity, of the US inflation data. Further, the performance of the proposed model compares quite favorably with, for example, ARMA and ARFIMA models with GARCH errors characterized by normal, symmetric and skewed Student-t distributions.

Keywords:

Conditional heteroskedasticity, Skewness, Inflation, Long memory, Normal mixture

Acknowledgments

The authors would like to thank Haas Markus and Juri Marcucci for their codes that were incorporated into the computer programs for our exercise, and the referees and Bong-Han Kim for their constructive comments and suggestions. Chung acknowledges the financial support from the Inje Research Grant in 2008.

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