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A long memory model with mixed normal GARCH for US inflation data

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Issue Date
2009-07
Publisher
Institute of Economic Research, Seoul National University
Citation
Seoul Journal of Economics, Vol.22 No.3, pp. 289-310
Keywords
Conditional heteroskedasticitySkewnessInflationLong memoryNormal mixture
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.
ISSN
1225-0279
Language
English
URI
https://hdl.handle.net/10371/67706
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College of Social Sciences (사회과학대학)Institute of Economics Research (경제연구소)Seoul Journal of Economics (SJE)Seoul Journal of Economics vol.22 no.1~4 (2009)
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