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Title:A long memory model with mixed normal GARCH for US inflation data PDF Logo
Authors:Cheung, Yin-wong
Chung, Sang-Kuck
Issue Date:2009
Series/Report no.:Working Papers, Santa Cruz Center for International Economics 09-10
Abstract:We introduce a time series model that captures both long memory and conditional heteroskedasticity and assess their 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.
Subjects:conditional heteroskedasticity
long memory
normal mixture
Document Type:Working Paper
Appears in Collections:Working Papers, Santa Cruz Institute for International Economics, UC Santa Cruz

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