The influence of covariance Hankel matrix dimension on algorithms for VARMA models

Some methods for estimating VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. Some authors suggest taking a larger dimension than theoretically necessary for this matrix. If the data sample is populous enough and the Hankel matrix dimension is unnecess...

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Autores: Pestano Gabino, Celina, González Concepción, Concepción Nieves, Gil Fariña, María Candelaria
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad de La Laguna (ULL)
Repositorio:RIULL. Repositorio Institucional de la Universidad de La Laguna
OAI Identifier:oai:riull.ull.es:915/41409
Acceso en línea:http://riull.ull.es/xmlui/handle/915/41409
Access Level:acceso abierto
Palabra clave:Covariance Hankel matrices
Vector Autoregressive Moving-Average (VARMA) models
vectorvalued linear stochastic systems
simulated models
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spelling The influence of covariance Hankel matrix dimension on algorithms for VARMA modelsPestano Gabino, CelinaGonzález Concepción, Concepción NievesGil Fariña, María CandelariaCovariance Hankel matricesVector Autoregressive Moving-Average (VARMA) modelsvectorvalued linear stochastic systemssimulated modelsSome methods for estimating VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. Some authors suggest taking a larger dimension than theoretically necessary for this matrix. If the data sample is populous enough and the Hankel matrix dimension is unnecessarily large, this may result in an unnecessary number of computations, as well as in worse numerical and statistical results. We provide some theoretical results to know which is the Hankel matrix with the lowest dimension that is theoretically necessary and illustrate, with several simulated VARMA models, that using a dimension of the Hankel matrix greater than the theoretical minimal dimension proposed as valid does not necessarily lead to improved estimates. Although we use two algorithms, our main contributions are independent of the estimation method considered. We note that our paper does not include any comparisons between different algorithms for estimating VARMA models, as this is not our aim.Economía Aplicada y Métodos Cuantitativos202520252020info:eu-repo/semantics/articleapplication/pdfhttp://riull.ull.es/xmlui/handle/915/41409reponame:RIULL. Repositorio Institucional de la Universidad de La Lagunainstname:Universidad de La Laguna (ULL)InglésWSEAS Transactions on Mathematics, Volume 19, 2020info:eu-repo/semantics/openAccessoai:riull.ull.es:915/414092026-06-22T13:13:57Z
dc.title.none.fl_str_mv The influence of covariance Hankel matrix dimension on algorithms for VARMA models
title The influence of covariance Hankel matrix dimension on algorithms for VARMA models
spellingShingle The influence of covariance Hankel matrix dimension on algorithms for VARMA models
Pestano Gabino, Celina
Covariance Hankel matrices
Vector Autoregressive Moving-Average (VARMA) models
vectorvalued linear stochastic systems
simulated models
title_short The influence of covariance Hankel matrix dimension on algorithms for VARMA models
title_full The influence of covariance Hankel matrix dimension on algorithms for VARMA models
title_fullStr The influence of covariance Hankel matrix dimension on algorithms for VARMA models
title_full_unstemmed The influence of covariance Hankel matrix dimension on algorithms for VARMA models
title_sort The influence of covariance Hankel matrix dimension on algorithms for VARMA models
dc.creator.none.fl_str_mv Pestano Gabino, Celina
González Concepción, Concepción Nieves
Gil Fariña, María Candelaria
author Pestano Gabino, Celina
author_facet Pestano Gabino, Celina
González Concepción, Concepción Nieves
Gil Fariña, María Candelaria
author_role author
author2 González Concepción, Concepción Nieves
Gil Fariña, María Candelaria
author2_role author
author
dc.contributor.none.fl_str_mv Economía Aplicada y Métodos Cuantitativos
dc.subject.none.fl_str_mv Covariance Hankel matrices
Vector Autoregressive Moving-Average (VARMA) models
vectorvalued linear stochastic systems
simulated models
topic Covariance Hankel matrices
Vector Autoregressive Moving-Average (VARMA) models
vectorvalued linear stochastic systems
simulated models
description Some methods for estimating VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. Some authors suggest taking a larger dimension than theoretically necessary for this matrix. If the data sample is populous enough and the Hankel matrix dimension is unnecessarily large, this may result in an unnecessary number of computations, as well as in worse numerical and statistical results. We provide some theoretical results to know which is the Hankel matrix with the lowest dimension that is theoretically necessary and illustrate, with several simulated VARMA models, that using a dimension of the Hankel matrix greater than the theoretical minimal dimension proposed as valid does not necessarily lead to improved estimates. Although we use two algorithms, our main contributions are independent of the estimation method considered. We note that our paper does not include any comparisons between different algorithms for estimating VARMA models, as this is not our aim.
publishDate 2020
dc.date.none.fl_str_mv 2020
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://riull.ull.es/xmlui/handle/915/41409
url http://riull.ull.es/xmlui/handle/915/41409
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv WSEAS Transactions on Mathematics, Volume 19, 2020
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIULL. Repositorio Institucional de la Universidad de La Laguna
instname:Universidad de La Laguna (ULL)
instname_str Universidad de La Laguna (ULL)
reponame_str RIULL. Repositorio Institucional de la Universidad de La Laguna
collection RIULL. Repositorio Institucional de la Universidad de La Laguna
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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