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dc.contributor.authorMeyer, Marcoen
dc.contributor.authorKreiss, Jens‐Peteren
dc.contributor.authorCavaliere, Giuseppeen
dc.contributor.authorPolitis, Dimitris Nicolasen
dc.contributor.authorRahbek, Andersen
dc.creatorMeyer, Marcoen
dc.creatorKreiss, Jens‐Peteren
dc.creatorCavaliere, Giuseppeen
dc.creatorPolitis, Dimitris Nicolasen
dc.creatorRahbek, Andersen
dc.date.accessioned2019-12-02T10:37:02Z
dc.date.available2019-12-02T10:37:02Z
dc.date.issued2014
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/57301
dc.source.urihttps://nls.ldls.org.uk/welcome.html?lsidyv921893cb
dc.titleOn the Vector Autoregressive Sieve Bootstrapen
dc.typeinfo:eu-repo/semantics/article
dc.description.startingpage1
dc.description.endingpageonline
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeArticleen
dc.description.notes<p>ID: 994en
dc.description.notesIn: JOURNAL OF TIME SERIES ANALYSIS volume 36 issue 3 page 377.en
dc.description.notesSummary: Abstract The concept of autoregressive sieve bootstrap is investigated for the case of vector autoregressive (VAR) time series. This procedure fits a finite‐order VAR model to the given data and generates residual‐based bootstrap replicates of the time series. The paper explores the range of validity of this resampling procedure and provides a general check criterion, which allows to decide whether the VAR sieve bootstrap asymptotically works for a specific statistic or not. In the latter case, we will point out the exact reason that causes the bootstrap to fail. The developed check criterion is then applied to some particularly interesting statistics..</p>en
dc.contributor.orcidCavaliere, Giuseppe [0000-0002-2856-0005]
dc.gnosis.orcid0000-0002-2856-0005


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