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Giraitis, Liudas
Kokoszka, Piotr
Leipus, Remigijus
Teyssière, Gilles
Year of Publication: 
Series/Report no.: 
SFB 373 Discussion Paper No. 1999,81
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.
long memory
ARCH models
semiparametric estimation
modified R/S
KPSS and V/S statistics
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Document Type: 
Working Paper

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