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2021-07-28Zeitschriftenartikel DOI: 10.1002/for.2802
A Bayesian time-varying autoregressive model for improved short‐term and long‐term prediction
dc.contributor.authorBerninger, Christoph
dc.contributor.authorStöcker, Almond
dc.contributor.authorRügamer, David
dc.date.accessioned2022-04-22T14:03:01Z
dc.date.available2022-04-22T14:03:01Z
dc.date.issued2021-07-28none
dc.date.updated2022-03-21T02:53:27Z
dc.identifier.issn0277-6693
dc.identifier.urihttp://edoc.hu-berlin.de/18452/25199
dc.description.abstractMotivated by the application to German interest rates, we propose a time‐varying autoregressive model for short‐term and long‐term prediction of time series that exhibit a temporary nonstationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC‐based inference by deriving relevant full conditional distributions and employ a Metropolis‐Hastings within Gibbs sampler approach to sample from the posterior (predictive) distribution. In combining data‐driven short‐term predictions with long‐term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to that of a 2‐Additive‐Factor Gaussian model as well as to the predictions of a dynamic Nelson‐Siegel model.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian time‐varying autoregressive modelseng
dc.subjectGibbs samplereng
dc.subjectinterest rate modelseng
dc.subjectlong run regularizationeng
dc.subjectMCMC metropolis‐Hastingseng
dc.subject.ddc510 Mathematiknone
dc.titleA Bayesian time-varying autoregressive model for improved short‐term and long‐term predictionnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/25199-3
dc.identifier.doi10.1002/for.2802none
dc.identifier.doihttp://dx.doi.org/10.18452/24528
dc.type.versionpublishedVersionnone
local.edoc.container-titleJournal of forecastingnone
local.edoc.pages20none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionWirtschaftswissenschaftliche Fakultätnone
local.edoc.container-publisher-nameWileynone
local.edoc.container-publisher-placeNew York, NYnone
local.edoc.container-volume41none
local.edoc.container-issue1none
local.edoc.container-firstpage181none
local.edoc.container-lastpage200none
dc.description.versionPeer Reviewednone
dc.identifier.eissn1099-131X

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