Semiparametric estimation of the intensity of long memory in conditional heteroskedasticity

Liudas Giraitis (), Piotr Kokoszka, Remigijus Leipus and Gilles Teyssière

No 1999,81, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Abstract: The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classical R/S analysis as well as spectral domain approximate maximum likelihood estimators. The finite sample performance of the estimators is examined by means of a Monte Carlo study.

Keywords: long memory; ARCH models; semiparametric estimation; modified R/S; KPSS and V/S statistics; periodogram (search for similar items in EconPapers)
Date: 1999
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Related works:
Journal Article: Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity (2000)
Working Paper: Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:199981

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