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Rate optimal semiparametric estimation of the memory parameter of the Gaussian time series with long-range dependence

By Liudas Giraitis, Peter M. Robinson and Alexander Samarov


There exist several estimators of the memory parameter in long-memory time series models with mean µ 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

Topics: HB Economic Theory
Publisher: Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science
Year: 1997
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Provided by: LSE Research Online
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