On a partly linear autoregressive model with moving average errors

Autores
Bianco, Ana Maria; Boente Boente, Graciela Lina
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we generalise the partly linear autoregression model considered in the literature by including moving average errors when we want to allow a large dependence to the past observations. The strong ergodicity of the process is derived. A consistent procedure to estimate the parametric and nonparametric components is provided together with a test statistic that allows to check the presence of a moving average component in the model. Also, a Monte Carlo study is carried out to check the performance of the given proposals.
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Ergodicity
Fisher-Consistency
Moving Average Errors
Partly Linear Autoregression
Smoothing Techniques
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/16546
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/16546
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling On a partly linear autoregressive model with moving average errorsBianco, Ana MariaBoente Boente, Graciela LinaErgodicityFisher-ConsistencyMoving Average ErrorsPartly Linear AutoregressionSmoothing Techniqueshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper, we generalise the partly linear autoregression model considered in the literature by including moving average errors when we want to allow a large dependence to the past observations. The strong ergodicity of the process is derived. A consistent procedure to estimate the parametric and nonparametric components is provided together with a test statistic that allows to check the presence of a moving average component in the model. Also, a Monte Carlo study is carried out to check the performance of the given proposals.Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaTaylor & Francis2010-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/16546Bianco, Ana Maria; Boente Boente, Graciela Lina; On a partly linear autoregressive model with moving average errors; Taylor & Francis; Journal Of Nonparametric Statistics; 22; 6; 8-2010; 797-8201048-52521029-0311enginfo:eu-repo/semantics/altIdentifier/doi/10.1080/10485250903469744info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/10485250903469744info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:35:49Zoai:ri.conicet.gov.ar:11336/16546instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:35:49.517CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On a partly linear autoregressive model with moving average errors
title On a partly linear autoregressive model with moving average errors
spellingShingle On a partly linear autoregressive model with moving average errors
Bianco, Ana Maria
Ergodicity
Fisher-Consistency
Moving Average Errors
Partly Linear Autoregression
Smoothing Techniques
title_short On a partly linear autoregressive model with moving average errors
title_full On a partly linear autoregressive model with moving average errors
title_fullStr On a partly linear autoregressive model with moving average errors
title_full_unstemmed On a partly linear autoregressive model with moving average errors
title_sort On a partly linear autoregressive model with moving average errors
dc.creator.none.fl_str_mv Bianco, Ana Maria
Boente Boente, Graciela Lina
author Bianco, Ana Maria
author_facet Bianco, Ana Maria
Boente Boente, Graciela Lina
author_role author
author2 Boente Boente, Graciela Lina
author2_role author
dc.subject.none.fl_str_mv Ergodicity
Fisher-Consistency
Moving Average Errors
Partly Linear Autoregression
Smoothing Techniques
topic Ergodicity
Fisher-Consistency
Moving Average Errors
Partly Linear Autoregression
Smoothing Techniques
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, we generalise the partly linear autoregression model considered in the literature by including moving average errors when we want to allow a large dependence to the past observations. The strong ergodicity of the process is derived. A consistent procedure to estimate the parametric and nonparametric components is provided together with a test statistic that allows to check the presence of a moving average component in the model. Also, a Monte Carlo study is carried out to check the performance of the given proposals.
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In this paper, we generalise the partly linear autoregression model considered in the literature by including moving average errors when we want to allow a large dependence to the past observations. The strong ergodicity of the process is derived. A consistent procedure to estimate the parametric and nonparametric components is provided together with a test statistic that allows to check the presence of a moving average component in the model. Also, a Monte Carlo study is carried out to check the performance of the given proposals.
publishDate 2010
dc.date.none.fl_str_mv 2010-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/16546
Bianco, Ana Maria; Boente Boente, Graciela Lina; On a partly linear autoregressive model with moving average errors; Taylor & Francis; Journal Of Nonparametric Statistics; 22; 6; 8-2010; 797-820
1048-5252
1029-0311
url http://hdl.handle.net/11336/16546
identifier_str_mv Bianco, Ana Maria; Boente Boente, Graciela Lina; On a partly linear autoregressive model with moving average errors; Taylor & Francis; Journal Of Nonparametric Statistics; 22; 6; 8-2010; 797-820
1048-5252
1029-0311
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/10485250903469744
info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/10485250903469744
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781995077599232
score 12.982451

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