eprintid: 20900
rev_number: 12
eprint_status: archive
userid: 2326
dir: disk0/00/02/09/00
datestamp: 2016-06-09 08:54:59
lastmod: 2016-06-24 08:57:17
status_changed: 2016-06-09 08:54:59
type: workingPaper
metadata_visibility: show
creators_name: Giraitis, Liudas
creators_name: Robinson, Peter M.
creators_name: Samarov, Alexander
title: Rate Optimal Semiparametric Estimationof the Memory Parameter of the Gaussian Time Series with Long Range Dependence
subjects: ddc-510
divisions: i-110400
abstract: There exist several estimators of the memory parameter in long-memorytime series models with mean mu and the spectrum specified only locally near zerofrequency. In this paper we give a lower bound for the rate of convergence of anyestimator of the memory parameter as a function of the degree of local smoothnessof the spectral density at zero. The lower bound allows one to evaluate andcompare different estimators by their asymptotic behavior, and to claim the rateoptimality for any estimator attaining the bound. A log-periodogram regressionestimator, analysed by Robinson (1992), is then shown to attain the lower bound,and is thus rate optimal.
date: 1995-05
id_scheme: DOI
id_number: 10.11588/heidok.00020900
schriftenreihe_cluster_id: sr-10a
schriftenreihe_order: 28
ppn_swb: 1657257134
own_urn: urn:nbn:de:bsz:16-heidok-209007
language: eng
bibsort: GIRAITISLIRATEOPTIMA199505
full_text_status: public
place_of_pub: Heidelberg
pages: 12
citation:   Giraitis, Liudas ; Robinson, Peter M. ; Samarov, Alexander  (1995) Rate Optimal Semiparametric Estimationof the Memory Parameter of the Gaussian Time Series with Long Range Dependence.  [Working paper]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/20900/1/beitrag.28.pdf