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2014-01-09Buch DOI: 10.18452/4491
Structural Vector Autoregressive Analysis in a DataRich Environment
dc.contributor.authorLütkepohl, Helmus
dc.date.accessioned2017-06-16T00:56:34Z
dc.date.available2017-06-16T00:56:34Z
dc.date.created2014-06-12
dc.date.issued2014-01-09
dc.date.submitted2014-01-09
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5143
dc.description.abstractLarge panels of variables are used by policy makers in deciding on policy actions. Therefore it is desirable to include large information sets in models for economic analysis. In this survey methods are reviewed for accounting for the information in large sets of variables in vector autoregressive (VAR) models. This can be done by aggregating the variables or by reducing the parameter space to a manageable dimension. Factor models reduce the space of variables whereas large Bayesian VAR models and panel VARs reduce the parameter space. Global VARs use a mixed approach. They aggregate the variables and use a parsimonious parametrisation. All these methods are discussed in this survey although the main emphasize is on factor models.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectpanel dataeng
dc.subjectfactor modelseng
dc.subjectstructural vector autoregressive modeleng
dc.subjectglobal vector autoregressioneng
dc.subjectBayesian vector autoregressioneng
dc.subject.ddc310 Statistik
dc.subject.ddc330 Wirtschaft
dc.titleStructural Vector Autoregressive Analysis in a DataRich Environment
dc.typebook
dc.subtitleA Survey
dc.identifier.urnurn:nbn:de:kobv:11-100217994
dc.identifier.doihttp://dx.doi.org/10.18452/4491
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages50
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2014
local.edoc.container-issue4
local.edoc.container-year2014
local.edoc.container-erstkatid2195055-6

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