Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity
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 classicla 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.
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Volume (Year): 3 (2000)
Issue (Month): 1 (January)
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References listed on IDEAS
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