|This centre is a member of The LSE Research Laboratory [RLAB]: CASE | CEE | CEP | FMG | SERC | STICERD||Cookies?|
Peter M Robinson,
Paper No' EM/1997/323:
Save Reference as: BibTeX File | EndNote Import File
Keywords: Long-range dependence; semiparametric models; optimal rates of convergence; lower bounds
Is hard copy/paper copy available? NO - Paper Copy Out Of Print.
This Paper is published under the following series: Econometrics
Share this page: Google Bookmarks | Facebook | Twitter
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.
Copyright © STICERD & LSE 2005 - 2015 | LSE, Houghton Street, London WC2A 2AE | Tel: +44(0)20 7955 6699 | Email: firstname.lastname@example.org | Site updated 06 March 2015