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dc.contributor.authorDjogbenou, Antoineen
dc.contributor.authorGonçalves, Sílviaen
dc.contributor.authorPerron, Benoiten
dc.contributor.authorCavaliere, Giuseppeen
dc.contributor.authorPolitis, Dimitris Nicolasen
dc.contributor.authorRahbek, Andersen
dc.creatorDjogbenou, Antoineen
dc.creatorGonçalves, Sílviaen
dc.creatorPerron, Benoiten
dc.creatorCavaliere, Giuseppeen
dc.creatorPolitis, Dimitris Nicolasen
dc.creatorRahbek, Andersen
dc.date.accessioned2019-12-02T10:34:53Z
dc.date.available2019-12-02T10:34:53Z
dc.date.issued2015
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/56756
dc.source.urihttps://nls.ldls.org.uk/welcome.html?lsidyv921893d3
dc.titleBootstrap Inference in Regressions with Estimated Factors and Serial Correlationen
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: 995en
dc.description.notesIn: JOURNAL OF TIME SERIES ANALYSIS volume 36 issue 3 page 481.en
dc.description.notesSummary: Abstract This article considers bootstrap inference in a factor‐augmented regression context where the errors could potentially be serially correlated. This generalizes results in Gonçalves & Perron (2014) and makes the bootstrap applicable to forecasting contexts where the forecast horizon is greater than one. We propose and justify two residual‐based approaches, a block wild bootstrap and a dependent wild bootstrap. Our simulations document improvement in coverage rates of confidence intervals for the coefficients when using block wild bootstrap or dependent wild bootstrap relative to both asymptotic theory and the wild bootstrap when serial correlation is present in the regression errors..</p>en
dc.contributor.orcidCavaliere, Giuseppe [0000-0002-2856-0005]
dc.gnosis.orcid0000-0002-2856-0005


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