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Abstract for:

Rate Optimal Semiparametric Estimation of the Memory Parameter of the Gaussian Time Serieswith Long-Range Dependence - (Now published in 'Journal of Time Series Analysis', 18 (1997), pp.49-60.)

Liudas Giraitis, Peter M Robinson, Alexander Samarov, February 1997
Paper No' EM/1997/323:
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Keywords: Long-range dependence; semiparametric models; optimal rates of convergence; lower bounds

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Is hard copy/paper copy available? NO - Paper Copy Out Of Print.
This Paper is published under the following series: Econometrics
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Abstract:

There exist several estimators of the memory parameter in long-memory time series models with mean and the spectrum specified only locally near zero frequency. In this paper we give a lower bound for the rate of convergence of any estimator of the memory parameter as a function of the degree of local smoothness of the spectral density at zero. The lower bound allows one to evaluate and compare different estimators by their asymptotic behaviour, and to claim the rate optimality for any estimator attaining the bound. The log-periodogram regression estimator, analysed by Robinson (1992), is then shown to attain the lower bound, and is thus rate optimal.