Estimation and forecasting in SUINAR(1) model
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/4429 |
Resumo: | This work considers a generalization of the INAR(1) model to the panel data first order Seemingly Unrelated INteger AutoRegressive Poisson model, SUINAR(1). It presents Bayesian and classical methodologies to estimate the parameters of Poisson SUINAR(1) model and to forecast future observations of the process. In particular, prediction intervals for forecasts - classical approach - and HPD prediction intervals - Bayesian approach - are derived. A simulation study is provided to give additional insight into the finite sample behaviour of the parameter estimates and forecasts. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Estimation and forecasting in SUINAR(1) modelforecastsGibbs samplingINAR modepanel dataThis work considers a generalization of the INAR(1) model to the panel data first order Seemingly Unrelated INteger AutoRegressive Poisson model, SUINAR(1). It presents Bayesian and classical methodologies to estimate the parameters of Poisson SUINAR(1) model and to forecast future observations of the process. In particular, prediction intervals for forecasts - classical approach - and HPD prediction intervals - Bayesian approach - are derived. A simulation study is provided to give additional insight into the finite sample behaviour of the parameter estimates and forecasts.Instituto Nacional de Estatística2011-11-28T15:48:29Z2008-01-01T00:00:00Z2008info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/4429eng1645-6726Silva, NPereira, ISilva, MEinfo: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:RCAAP2024-02-22T11:04:54Zoai:ria.ua.pt:10773/4429Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:42:21.097314Repositório 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 |
Estimation and forecasting in SUINAR(1) model |
title |
Estimation and forecasting in SUINAR(1) model |
spellingShingle |
Estimation and forecasting in SUINAR(1) model Silva, N forecasts Gibbs sampling INAR mode panel data |
title_short |
Estimation and forecasting in SUINAR(1) model |
title_full |
Estimation and forecasting in SUINAR(1) model |
title_fullStr |
Estimation and forecasting in SUINAR(1) model |
title_full_unstemmed |
Estimation and forecasting in SUINAR(1) model |
title_sort |
Estimation and forecasting in SUINAR(1) model |
author |
Silva, N |
author_facet |
Silva, N Pereira, I Silva, ME |
author_role |
author |
author2 |
Pereira, I Silva, ME |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Silva, N Pereira, I Silva, ME |
dc.subject.por.fl_str_mv |
forecasts Gibbs sampling INAR mode panel data |
topic |
forecasts Gibbs sampling INAR mode panel data |
description |
This work considers a generalization of the INAR(1) model to the panel data first order Seemingly Unrelated INteger AutoRegressive Poisson model, SUINAR(1). It presents Bayesian and classical methodologies to estimate the parameters of Poisson SUINAR(1) model and to forecast future observations of the process. In particular, prediction intervals for forecasts - classical approach - and HPD prediction intervals - Bayesian approach - are derived. A simulation study is provided to give additional insight into the finite sample behaviour of the parameter estimates and forecasts. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01T00:00:00Z 2008 2011-11-28T15:48:29Z |
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/10773/4429 |
url |
http://hdl.handle.net/10773/4429 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1645-6726 |
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 |
Instituto Nacional de Estatística |
publisher.none.fl_str_mv |
Instituto Nacional de Estatística |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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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1799137471539183616 |