Abstract
The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis et al. (1999b). We consider estimation 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. We review relevant theoretical results and present an empirical simulation study.
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Giraitis, L., Kokoszka, P., Leipus, R. et al. Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity. Statistical Inference for Stochastic Processes 3, 113–128 (2000). https://doi.org/10.1023/A:1009951213271
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DOI: https://doi.org/10.1023/A:1009951213271