Bayesian prediction in threshold autoregressive models with exponential white noise

Detalhes bibliográficos
Autor(a) principal: Pereira, IMS
Data de Publicação: 2004
Outros Autores: Amaral-Turkman, MA
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/4433
Resumo: In this paper, we develop a Bayesian analysis of a threshold antoregressive model with exponential noise. An approximate Bayes methodology, which is introduced here; and the Gibbs sampler are used to compute marginal posterior densities for the parameters of the model; including the threshold parameter, and to compute one-step-ahead predictive density functions. The proposed methodology is illustrated with a simulation study and a real example.
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spelling Bayesian prediction in threshold autoregressive models with exponential white noiseThreshold modelBayesian predictionGibbs samplerIn this paper, we develop a Bayesian analysis of a threshold antoregressive model with exponential noise. An approximate Bayes methodology, which is introduced here; and the Gibbs sampler are used to compute marginal posterior densities for the parameters of the model; including the threshold parameter, and to compute one-step-ahead predictive density functions. The proposed methodology is illustrated with a simulation study and a real example.Springer Verlag2011-11-28T16:19:36Z2004-01-01T00:00:00Z2004info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/4433eng1133-0686Pereira, IMSAmaral-Turkman, MAinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:04:55Zoai:ria.ua.pt:10773/4433Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:42:21.277202Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Bayesian prediction in threshold autoregressive models with exponential white noise
title Bayesian prediction in threshold autoregressive models with exponential white noise
spellingShingle Bayesian prediction in threshold autoregressive models with exponential white noise
Pereira, IMS
Threshold model
Bayesian prediction
Gibbs sampler
title_short Bayesian prediction in threshold autoregressive models with exponential white noise
title_full Bayesian prediction in threshold autoregressive models with exponential white noise
title_fullStr Bayesian prediction in threshold autoregressive models with exponential white noise
title_full_unstemmed Bayesian prediction in threshold autoregressive models with exponential white noise
title_sort Bayesian prediction in threshold autoregressive models with exponential white noise
author Pereira, IMS
author_facet Pereira, IMS
Amaral-Turkman, MA
author_role author
author2 Amaral-Turkman, MA
author2_role author
dc.contributor.author.fl_str_mv Pereira, IMS
Amaral-Turkman, MA
dc.subject.por.fl_str_mv Threshold model
Bayesian prediction
Gibbs sampler
topic Threshold model
Bayesian prediction
Gibbs sampler
description In this paper, we develop a Bayesian analysis of a threshold antoregressive model with exponential noise. An approximate Bayes methodology, which is introduced here; and the Gibbs sampler are used to compute marginal posterior densities for the parameters of the model; including the threshold parameter, and to compute one-step-ahead predictive density functions. The proposed methodology is illustrated with a simulation study and a real example.
publishDate 2004
dc.date.none.fl_str_mv 2004-01-01T00:00:00Z
2004
2011-11-28T16:19:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/4433
url http://hdl.handle.net/10773/4433
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1133-0686
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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