Validity Of The Sampling Window Method For Long-Range Dependent Linear Processes
Abstractthe sampling window method of hall, jing, and lahiri (1998, statistica sinica 8, 1189 1204) is known to consistently estimate the distribution of the sample mean for a class of long-range dependent processes, generated by transformations of gaussian time series. this paper shows that the same nonparametric subsampling method is also valid for an entirely different category of long-range dependent series that are linear with possibly non-gaussian innovations. for these strongly dependent time processes, subsampling confidence intervals allow inference on the process mean without knowledge of the underlying innovation distribution or the long-memory parameter. the finite-sample coverage accuracy of the subsampling method is examined through a numerical study.the authors thank two referees for comments and suggestions that greatly improved an earlier draft of the paper. this research was partially supported by u.s. national science foundation grants dms 00-72571 and dms 03-06574 and by the deutsche forschungsgemeinschaft (sfb 475).
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 21 (2005)
Issue (Month): 06 (December)
Contact details of provider:
Postal: The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU UK
Fax: +44 (0)1223 325150
Web page: http://journals.cambridge.org/jid_ECTProvider-Email:email@example.com
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Ting Zhang & Hwai-Chung Ho & Martin Wendler & Wei Biao Wu, 2013. "Block Sampling under Strong Dependence," Papers 1312.5807, arXiv.org.
- Daniel J. Nordman & Philipp Sibbertsen & Soumendra N. Lahiri, 2007.
"Empirical likelihood confidence intervals for the mean of a long-range dependent process,"
Journal of Time Series Analysis,
Wiley Blackwell, vol. 28(4), pages 576-599, 07.
- Nordman, Dan Nordman & Sibbertsen, Philipp & Lahiri, Soumendra N., 2005. "Empirical likelihood confidence intervals for the mean of a long-range dependent process," Hannover Economic Papers (HEP) dp-327, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Zhang, Ting & Ho, Hwai-Chung & Wendler, Martin & Wu, Wei Biao, 2013. "Block sampling under strong dependence," Stochastic Processes and their Applications, Elsevier, vol. 123(6), pages 2323-2339.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Keith Waters).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.