TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution

This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t-distribution. The analysis is done in the presence of missing data in both the threshold process {Zt} and the interest process {Xt}. We develop a three-stage...

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Autores:
Zhang, Hanwen
Nieto, Fabio H.
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/66551
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/66551
http://bdigital.unal.edu.co/67579/
Palabra clave:
51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Bayesian Statistics
Gibbs Sampler
Missing Data
Forecasting
Time Series
Threshold Autoregressive Model
Datos faltantes
Estadística Bayesiana
Modelo autoregresivo de umbrales
Muestreador de Gibbs
Pronóstico
Series de tiempo
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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oai_identifier_str oai:repositorio.unal.edu.co:unal/66551
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repository_id_str
spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Zhang, Hanwenb4ea95f0-8c15-4fd3-bc81-040e58b6d4e8300Nieto, Fabio H.ded47b7c-a2b1-414b-9f06-1743735a83543002019-07-03T02:21:20Z2019-07-03T02:21:20Z2015-01-01ISSN: 2389-8976https://repositorio.unal.edu.co/handle/unal/66551http://bdigital.unal.edu.co/67579/This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t-distribution. The analysis is done in the presence of missing data in both the threshold process {Zt} and the interest process {Xt}. We develop a three-stage procedure based on the Gibbs sampler in order to identify and estimate the model. Additionally, the estimation of the missing data and the forecasting procedure are provided. The proposed methodology is illustrated with simulated and real-life data.En este trabajo consideramos el modelamiento de los modelos autoregresivos de umbrales (TAR) con datos faltantes tanto en la serie de umbrales como la serie de interés cuando el proceso del ruido blanco sigue una distribución t de student. Desarrollamos un procedimiento de tres etapas basado en el muestreador de Gibbs para identificar y estimar el modelo, además de la estimación de los datos faltantes y el procedimiento para el pronóstico. La metodología propuesta fue aplicada a datos simulados y datos reales.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Estadísticahttps://revistas.unal.edu.co/index.php/estad/article/view/48813Universidad Nacional de Colombia Revistas electrónicas UN Revista Colombiana de EstadísticaRevista Colombiana de EstadísticaZhang, Hanwen and Nieto, Fabio H. (2015) TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution. Revista Colombiana de Estadística, 38 (1). pp. 239-265. ISSN 2389-897651 Matemáticas / Mathematics31 Colecciones de estadística general / StatisticsBayesian StatisticsGibbs SamplerMissing DataForecastingTime SeriesThreshold Autoregressive ModelDatos faltantesEstadística BayesianaModelo autoregresivo de umbralesMuestreador de GibbsPronósticoSeries de tiempoTAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-DistributionArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL48813-239291-1-PB.pdfapplication/pdf2667696https://repositorio.unal.edu.co/bitstream/unal/66551/1/48813-239291-1-PB.pdf885a62f8a4155e88cfda184bfada484eMD51THUMBNAIL48813-239291-1-PB.pdf.jpg48813-239291-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg5465https://repositorio.unal.edu.co/bitstream/unal/66551/2/48813-239291-1-PB.pdf.jpge299418879492b43ff778352b4a649c1MD52unal/66551oai:repositorio.unal.edu.co:unal/665512024-05-16 23:09:45.19Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
title TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
spellingShingle TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Bayesian Statistics
Gibbs Sampler
Missing Data
Forecasting
Time Series
Threshold Autoregressive Model
Datos faltantes
Estadística Bayesiana
Modelo autoregresivo de umbrales
Muestreador de Gibbs
Pronóstico
Series de tiempo
title_short TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
title_full TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
title_fullStr TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
title_full_unstemmed TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
title_sort TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
dc.creator.fl_str_mv Zhang, Hanwen
Nieto, Fabio H.
dc.contributor.author.spa.fl_str_mv Zhang, Hanwen
Nieto, Fabio H.
dc.subject.ddc.spa.fl_str_mv 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
topic 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Bayesian Statistics
Gibbs Sampler
Missing Data
Forecasting
Time Series
Threshold Autoregressive Model
Datos faltantes
Estadística Bayesiana
Modelo autoregresivo de umbrales
Muestreador de Gibbs
Pronóstico
Series de tiempo
dc.subject.proposal.spa.fl_str_mv Bayesian Statistics
Gibbs Sampler
Missing Data
Forecasting
Time Series
Threshold Autoregressive Model
Datos faltantes
Estadística Bayesiana
Modelo autoregresivo de umbrales
Muestreador de Gibbs
Pronóstico
Series de tiempo
description This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t-distribution. The analysis is done in the presence of missing data in both the threshold process {Zt} and the interest process {Xt}. We develop a three-stage procedure based on the Gibbs sampler in order to identify and estimate the model. Additionally, the estimation of the missing data and the forecasting procedure are provided. The proposed methodology is illustrated with simulated and real-life data.
publishDate 2015
dc.date.issued.spa.fl_str_mv 2015-01-01
dc.date.accessioned.spa.fl_str_mv 2019-07-03T02:21:20Z
dc.date.available.spa.fl_str_mv 2019-07-03T02:21:20Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.spa.fl_str_mv ISSN: 2389-8976
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/66551
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/67579/
identifier_str_mv ISSN: 2389-8976
url https://repositorio.unal.edu.co/handle/unal/66551
http://bdigital.unal.edu.co/67579/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/estad/article/view/48813
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Revista Colombiana de Estadística
Revista Colombiana de Estadística
dc.relation.references.spa.fl_str_mv Zhang, Hanwen and Nieto, Fabio H. (2015) TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution. Revista Colombiana de Estadística, 38 (1). pp. 239-265. ISSN 2389-8976
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Estadística
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/66551/1/48813-239291-1-PB.pdf
https://repositorio.unal.edu.co/bitstream/unal/66551/2/48813-239291-1-PB.pdf.jpg
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
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