Temporal series for random parameter models: a bayesian approach
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
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/2552 |
Resumo: | This paper presents a Bayesian approach to make inference about the parameters of autoregressive models. In this context, when the parameters of models are independent and vary at random we consider a hierarchical model to describe the a posteriori density of parameters. A second approach assumes that the parameters of model vary according to a first order autoregressive model. In this case, the proposed approach is seen as an extension of Kalman filter where the variances of noises are known. The models were analysed using Monte Carlo simulation techniques and the resulting samples of a posteriori densities allowed to foresee a data series through predictable densities. Ilustrations with actual data of a financial series are showed and the two models are evaluated by the quality of the prediction obtained, emphasizing the best model which represents the data. |
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Acta scientiarum. Technology (Online) |
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Temporal series for random parameter models: a bayesian approachSéries temporais para modelos com parâmetros aleatórios: uma abordagem bayesianaprocesso auto-regressivoinferência bayesianamodelo hierárquicomodelo dinâmicofiltro de KalmanGibbs-SamplingMetropolis-Hasting1.02.02.00-5 EstatísticaThis paper presents a Bayesian approach to make inference about the parameters of autoregressive models. In this context, when the parameters of models are independent and vary at random we consider a hierarchical model to describe the a posteriori density of parameters. A second approach assumes that the parameters of model vary according to a first order autoregressive model. In this case, the proposed approach is seen as an extension of Kalman filter where the variances of noises are known. The models were analysed using Monte Carlo simulation techniques and the resulting samples of a posteriori densities allowed to foresee a data series through predictable densities. Ilustrations with actual data of a financial series are showed and the two models are evaluated by the quality of the prediction obtained, emphasizing the best model which represents the data.Este trabalho apresenta uma abordagem bayesiana para fazer inferência sobre os parâmetros de modelos auto-regressivos. Nesse contexto, quando os parâmetros variam de forma aleatória e independente, adotou-se um modelo hierárquico para descrever a densidade a posteriori. Uma segunda abordagem supõe que os parâmetros variam de acordo com um modelo auto-regressivo de primeira ordem; nesse caso a abordagem é vista como uma extensão do filtro de Kalman, no qual as variâncias dos ruídos são conhecidas. Os modelos foram analisados usando técnicas de simulação de Monte Carlo e a geração de amostras das densidades a posteriori permitiram fazer previsões de séries por intermédio das densidades preditivas Para exemplificar a aplicação dos modelos, consideramos uma série real correspondente ao preço de ações.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/255210.4025/actascitechnol.v24i0.2552Acta Scientiarum. Technology; Vol 24 (2002); 1745-1753Acta Scientiarum. Technology; v. 24 (2002); 1745-17531806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2552/1572Mena, LeonilceAndrade Filho, Marinho Gomes deinfo:eu-repo/semantics/openAccess2024-05-17T13:02:46Zoai:periodicos.uem.br/ojs:article/2552Revistahttps://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 |
Temporal series for random parameter models: a bayesian approach Séries temporais para modelos com parâmetros aleatórios: uma abordagem bayesiana |
title |
Temporal series for random parameter models: a bayesian approach |
spellingShingle |
Temporal series for random parameter models: a bayesian approach Mena, Leonilce processo auto-regressivo inferência bayesiana modelo hierárquico modelo dinâmico filtro de Kalman Gibbs-Sampling Metropolis-Hasting 1.02.02.00-5 Estatística |
title_short |
Temporal series for random parameter models: a bayesian approach |
title_full |
Temporal series for random parameter models: a bayesian approach |
title_fullStr |
Temporal series for random parameter models: a bayesian approach |
title_full_unstemmed |
Temporal series for random parameter models: a bayesian approach |
title_sort |
Temporal series for random parameter models: a bayesian approach |
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 hierárquico modelo dinâmico filtro de Kalman Gibbs-Sampling Metropolis-Hasting 1.02.02.00-5 Estatística |
topic |
processo auto-regressivo inferência bayesiana modelo hierárquico modelo dinâmico filtro de Kalman Gibbs-Sampling Metropolis-Hasting 1.02.02.00-5 Estatística |
description |
This paper presents a Bayesian approach to make inference about the parameters of autoregressive models. In this context, when the parameters of models are independent and vary at random we consider a hierarchical model to describe the a posteriori density of parameters. A second approach assumes that the parameters of model vary according to a first order autoregressive model. In this case, the proposed approach is seen as an extension of Kalman filter where the variances of noises are known. The models were analysed using Monte Carlo simulation techniques and the resulting samples of a posteriori densities allowed to foresee a data series through predictable densities. Ilustrations with actual data of a financial series are showed and the two models are evaluated by the quality of the prediction obtained, emphasizing the best model which represents the data. |
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/2552 10.4025/actascitechnol.v24i0.2552 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2552 |
identifier_str_mv |
10.4025/actascitechnol.v24i0.2552 |
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/2552/1572 |
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); 1745-1753 Acta Scientiarum. Technology; v. 24 (2002); 1745-1753 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_ |
1799315332139057152 |