Skip to main content
Article thumbnail
Location of Repository

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

Abstract

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
OAI identifier: oai:eprints.lse.ac.uk:2175
Provided by: LSE Research Online
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://sticerd.lse.ac.uk (external link)
  • http://eprints.lse.ac.uk/2175/ (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.