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2016-03-21Diskussionspapier DOI: 10.18452/4627
Calculating Joint Confidence Bands for Impulse Response Functions using Highest Density Regions
dc.contributor.authorLütkepohl, Helmut
dc.contributor.authorStaszewska-Bystrova, Anna
dc.contributor.authorWinker, Peter
dc.date.accessioned2017-06-16T01:24:45Z
dc.date.available2017-06-16T01:24:45Z
dc.date.created2017-02-06
dc.date.issued2016-03-21
dc.date.submitted2016-03-21
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5279
dc.description.abstractThis paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models. The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Monte Carlo comparison of the HDR bands with existing alternatives shows that the former are competitive with the bootstrap-based Bonferroni and Wald confidence regions. The relative tightness of the HDR bands matched with their good coverage properties makes them attractive for applications. An application to corporate bond spreads for Germany highlights the potential for empirical work.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectvector autoregressive processeng
dc.subjectImpulse responseseng
dc.subjectjoint confidence bandseng
dc.subjecthighest density regioneng
dc.subject.ddc310 Sammlungen allgemeiner Statistiken
dc.subject.ddc330 Wirtschaft
dc.titleCalculating Joint Confidence Bands for Impulse Response Functions using Highest Density Regions
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-100243618
dc.identifier.doihttp://dx.doi.org/10.18452/4627
local.edoc.pages41
local.edoc.type-nameDiskussionspapier
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-year2016
dc.identifier.zdb2195055-6
bua.series.nameSonderforschungsbereich 649: Ökonomisches Risiko
bua.series.issuenumber2016,17

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