Note on the autoregressive spectral estimator

Autores
Mentz, Raul Pedro; Martinez, Carlos Ismael
Año de publicación
2006
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An estimator of the spectral density of a stationary time series is obtained by fitting to the observations an autoregressive model (often including the estimation of its order), and computing with sample values the spectrum of the indicated model. In the present note we consider the calculation of simultaneous confidence bands, according to Newton and Pagano (1984). The procedure is illustrated by means of Monte Carlo simulations, for series generated by autoregressive models or orders up to 5.  Key words and phrases. Confidence bands, asymptotic properties, Monte Carlo, Estimation of autoregressive order
Fil: Mentz, Raul Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas; Argentina
Fil: Martinez, Carlos Ismael. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina
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:11336/83565
Acceder
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spelling Note on the autoregressive spectral estimatorMentz, Raul PedroMartinez, Carlos Ismaelhttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5An estimator of the spectral density of a stationary time series is obtained by fitting to the observations an autoregressive model (often including the estimation of its order), and computing with sample values the spectrum of the indicated model. In the present note we consider the calculation of simultaneous confidence bands, according to Newton and Pagano (1984). The procedure is illustrated by means of Monte Carlo simulations, for series generated by autoregressive models or orders up to 5.  Key words and phrases. Confidence bands, asymptotic properties, Monte Carlo, Estimation of autoregressive orderFil: Mentz, Raul Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas; ArgentinaFil: Martinez, Carlos Ismael. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaBrazilian Statistical Association2006-12info: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/11336/83565Mentz, Raul Pedro; Martinez, Carlos Ismael; Note on the autoregressive spectral estimator; Brazilian Statistical Association; Brazilian Journal Of Probability And Statistics; 20; 1; 12-2006; 49-660103-0752CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.jstor.org/stable/43601073info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:13:22Zoai:ri.conicet.gov.ar:11336/83565instacron: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 11:13:22.377CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Note on the autoregressive spectral estimator
title Note on the autoregressive spectral estimator
spellingShingle Note on the autoregressive spectral estimator
Mentz, Raul Pedro
title_short Note on the autoregressive spectral estimator
title_full Note on the autoregressive spectral estimator
title_fullStr Note on the autoregressive spectral estimator
title_full_unstemmed Note on the autoregressive spectral estimator
title_sort Note on the autoregressive spectral estimator
dc.creator.none.fl_str_mv Mentz, Raul Pedro
Martinez, Carlos Ismael
author Mentz, Raul Pedro
author_facet Mentz, Raul Pedro
Martinez, Carlos Ismael
author_role author
author2 Martinez, Carlos Ismael
author2_role author
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv An estimator of the spectral density of a stationary time series is obtained by fitting to the observations an autoregressive model (often including the estimation of its order), and computing with sample values the spectrum of the indicated model. In the present note we consider the calculation of simultaneous confidence bands, according to Newton and Pagano (1984). The procedure is illustrated by means of Monte Carlo simulations, for series generated by autoregressive models or orders up to 5.  Key words and phrases. Confidence bands, asymptotic properties, Monte Carlo, Estimation of autoregressive order
Fil: Mentz, Raul Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas; Argentina
Fil: Martinez, Carlos Ismael. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina
description An estimator of the spectral density of a stationary time series is obtained by fitting to the observations an autoregressive model (often including the estimation of its order), and computing with sample values the spectrum of the indicated model. In the present note we consider the calculation of simultaneous confidence bands, according to Newton and Pagano (1984). The procedure is illustrated by means of Monte Carlo simulations, for series generated by autoregressive models or orders up to 5.  Key words and phrases. Confidence bands, asymptotic properties, Monte Carlo, Estimation of autoregressive order
publishDate 2006
dc.date.none.fl_str_mv 2006-12
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/11336/83565
Mentz, Raul Pedro; Martinez, Carlos Ismael; Note on the autoregressive spectral estimator; Brazilian Statistical Association; Brazilian Journal Of Probability And Statistics; 20; 1; 12-2006; 49-66
0103-0752
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83565
identifier_str_mv Mentz, Raul Pedro; Martinez, Carlos Ismael; Note on the autoregressive spectral estimator; Brazilian Statistical Association; Brazilian Journal Of Probability And Statistics; 20; 1; 12-2006; 49-66
0103-0752
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.jstor.org/stable/43601073
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 Brazilian Statistical Association
publisher.none.fl_str_mv Brazilian Statistical Association
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
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score 12.982451

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