Adjusted blockwise empirical likelihood for long memory time series models

Feifan Jiang and Lihong Wang ()
Additional contact information
Feifan Jiang: Nanjing University
Lihong Wang: Nanjing University

Statistical Methods & Applications, 2018, vol. 27, issue 2, 319-332

Abstract: Abstract In this paper, we introduce an adjusted blockwise empirical likelihood (ABEL) method for long memory time series models. By dividing time series into blocks and by adding an appropriate adjustment term, we construct the ABEL ratio and the confidence interval for the mean of the process. Under mild conditions, we show that Wilks’ theorem still holds for the ABEL ratio by choosing a specific block correction factor. The Monte Carlo simulation studies are reported to assess the finite sample performance of the proposed ABEL method.

Keywords: Adjusted blockwise empirical likelihood; Confidence interval; Long memory time series models; Wilks’ theorem; 62M10 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10260-017-0403-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0403-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla ().