Recent developments in bootstrap methods for dependent data

Giuseppe Cavaliere, Dimitris N. Politis, Anders Rahbek, Paul Doukhan, Gabriel Lang, Anne Leucht and Michael H. Neumann

Journal of Time Series Analysis, 2015, vol. 36, issue 3, 290-314

Abstract: type="main" xml:id="jtsa12106-abs-0001"> In this paper, we propose a model-free bootstrap method for the empirical process under absolute regularity. More precisely, consistency of an adapted version of the so-called dependent wild bootstrap, which was introduced by Shao ( ) and is very easy to implement, is proved under minimal conditions on the tuning parameter of the procedure. We show how our results can be applied to construct confidence intervals for unknown parameters and to approximate critical values for statistical tests. In a simulation study, we investigate the size properties of a bootstrap-aided Kolmogorov-Smirnov test and show that our method is competitive to standard block bootstrap methods in finite samples.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1111/jtsa.12106 (text/html)
Access to full text is restricted to subscribers.

Related works:
Journal Article: Recent developments in bootstrap methods for dependent data (2015)
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:bla:jtsera:v:36:y:2015:i:3:p:290-314

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().