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

정상국|Yin-Wong Cheung
경제연구소 (Hanyang Economic Research Institute)|2009.09|Seoul Journal of Economics|제22권 제3호|pp. 289-310|DOI 10.22904/sje.2009.22.3.001

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참고문헌

키워드

SkewnessInflationConditional heteroskedasticityLong memoryNormal mixture
주제분류경제학

초록

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.

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.

참고문헌 (36)

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