Dynamic model for a first order autoregression process with Bayesian methodology

Detalhes bibliográficos
Autor(a) principal: Mena, Leonilce
Data de Publicação: 2008
Outros Autores: Andrade Filho, Marinho Gomes de
Tipo de documento: Artigo
Idioma: por
Título da fonte: Acta scientiarum. Technology (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2553
Resumo: A ramification of a first order autoregression process is provided. It comprises randomized and variant coefficients in time and assumes a structure of dependency of randomized coefficients that leads towards adapted Kalman's Filter. Although the Kalman Filter model is a generalization of the ordinary Kalman Filter, its analysis produces technical difficulties. It does not seem to be impossible to find a closed form for the filter. Monte Carlo's simulation was applied to Markov's Chain by Gibbs-Sampling and Metropolis-Hasting algorithms to infer parameters of model and work out forecasts of data for a time series of indexes of shares and meat prices.
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spelling Dynamic model for a first order autoregression process with Bayesian methodologyModelo dinâmico para um processo auto-regressivo de primeira ordem, aplicando metodologia Bayesianaprocesso Auto-regressivoinferência Bayesianamodelo dinâmicofiltro de KalmanGibbs-SamplingMetropolis-Hastings1.02.02.00-5 EstatísticaA ramification of a first order autoregression process is provided. It comprises randomized and variant coefficients in time and assumes a structure of dependency of randomized coefficients that leads towards adapted Kalman's Filter. Although the Kalman Filter model is a generalization of the ordinary Kalman Filter, its analysis produces technical difficulties. It does not seem to be impossible to find a closed form for the filter. Monte Carlo's simulation was applied to Markov's Chain by Gibbs-Sampling and Metropolis-Hasting algorithms to infer parameters of model and work out forecasts of data for a time series of indexes of shares and meat prices.Neste artigo apresentamos uma ramificação do processo auto-regressivo de primeira ordem com coeficiente aleatório e variante no tempo, assumindo uma estrutura de dependência dos coeficientes aleatórios, que leva a um modelo de filtro de Kalman adaptado. Embora o modelo de filtro de Kalman considerado seja uma generalização do filtro de Kalman Ordinário, sua análise produz dificuldades técnicas, porque não é possível encontrar uma forma fechada para o filtro, assim aplicamos simulação de Monte Carlo em Cadeia de Markov utilizando os algoritmos Amostrador de Gibbs e Metropolis-Hasting para fazer inferência quanto aos parâmetros do modelo e também fazer previsões de dados de uma série temporal de índice de preços de ações e preço do boi gordo.Universidade Estadual De Maringá2008-04-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/255310.4025/actascitechnol.v24i0.2553Acta Scientiarum. Technology; Vol 24 (2002); 1755-1760Acta Scientiarum. Technology; v. 24 (2002); 1755-17601806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2553/1573Mena, LeonilceAndrade Filho, Marinho Gomes deinfo:eu-repo/semantics/openAccess2024-05-17T13:02:46Zoai:periodicos.uem.br/ojs:article/2553Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:02:46Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Dynamic model for a first order autoregression process with Bayesian methodology
Modelo dinâmico para um processo auto-regressivo de primeira ordem, aplicando metodologia Bayesiana
title Dynamic model for a first order autoregression process with Bayesian methodology
spellingShingle Dynamic model for a first order autoregression process with Bayesian methodology
Mena, Leonilce
processo Auto-regressivo
inferência Bayesiana
modelo dinâmico
filtro de Kalman
Gibbs-Sampling
Metropolis-Hastings
1.02.02.00-5 Estatística
title_short Dynamic model for a first order autoregression process with Bayesian methodology
title_full Dynamic model for a first order autoregression process with Bayesian methodology
title_fullStr Dynamic model for a first order autoregression process with Bayesian methodology
title_full_unstemmed Dynamic model for a first order autoregression process with Bayesian methodology
title_sort Dynamic model for a first order autoregression process with Bayesian methodology
author Mena, Leonilce
author_facet Mena, Leonilce
Andrade Filho, Marinho Gomes de
author_role author
author2 Andrade Filho, Marinho Gomes de
author2_role author
dc.contributor.author.fl_str_mv Mena, Leonilce
Andrade Filho, Marinho Gomes de
dc.subject.por.fl_str_mv processo Auto-regressivo
inferência Bayesiana
modelo dinâmico
filtro de Kalman
Gibbs-Sampling
Metropolis-Hastings
1.02.02.00-5 Estatística
topic processo Auto-regressivo
inferência Bayesiana
modelo dinâmico
filtro de Kalman
Gibbs-Sampling
Metropolis-Hastings
1.02.02.00-5 Estatística
description A ramification of a first order autoregression process is provided. It comprises randomized and variant coefficients in time and assumes a structure of dependency of randomized coefficients that leads towards adapted Kalman's Filter. Although the Kalman Filter model is a generalization of the ordinary Kalman Filter, its analysis produces technical difficulties. It does not seem to be impossible to find a closed form for the filter. Monte Carlo's simulation was applied to Markov's Chain by Gibbs-Sampling and Metropolis-Hasting algorithms to infer parameters of model and work out forecasts of data for a time series of indexes of shares and meat prices.
publishDate 2008
dc.date.none.fl_str_mv 2008-04-22
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2553
10.4025/actascitechnol.v24i0.2553
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2553
identifier_str_mv 10.4025/actascitechnol.v24i0.2553
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2553/1573
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 Universidade Estadual De Maringá
publisher.none.fl_str_mv Universidade Estadual De Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Technology; Vol 24 (2002); 1755-1760
Acta Scientiarum. Technology; v. 24 (2002); 1755-1760
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta scientiarum. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv ||actatech@uem.br
_version_ 1799315332140105728

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