Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms

Problems in estimating simultaneous equation models when error terms are not intertemporally uncorrelated has motivated the introduction of a new multivariate model referred to as Multilevel Simultaneous Equation Model (MSEM). The maximum likelihood estimation of the parameters of an MSEM has been s...

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Detalles Bibliográficos
Autores: González Espinosa, Martín, Hernández Sanjaime, Rocío, López-Espín, Jose J.
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/34947
Acceso en línea:https://hdl.handle.net/11000/34947
Access Level:acceso abierto
Palabra clave:multilevel simultaneous equation model
maximum likelihood estimation
genetic algorithms
optimization
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
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spelling Estimation of Multilevel Simultaneous Equation Models through Genetic AlgorithmsGonzález Espinosa, MartínHernández Sanjaime, RocíoLópez-Espín, Jose J.multilevel simultaneous equation modelmaximum likelihood estimationgenetic algorithmsoptimizationCDU::5 - Ciencias puras y naturales::51 - MatemáticasProblems in estimating simultaneous equation models when error terms are not intertemporally uncorrelated has motivated the introduction of a new multivariate model referred to as Multilevel Simultaneous Equation Model (MSEM). The maximum likelihood estimation of the parameters of an MSEM has been set forth. Because of the difficulties associated with the solution of the system of likelihood equations, the maximum likelihood estimator cannot be obtained through exhaustive search procedures. A hybrid metaheuristic that combines a genetic algorithm and an optimization method has been developed to overcome both technical and analytical limitations in the general case when the covariance structure is unknown. The behaviour of the hybrid metaheuristic has been discussed by varying different tuning parameters. A simulation study has been included to evaluate the adequacy of this estimator when error terms are not serially independent. Finally, the performance of this estimation approach has been compared with regard to other alternatives.MDPIDepartamentos de la UMH::Estadística, Matemáticas e Informática202520252020info:eu-repo/semantics/articleapplication/pdf12application/pdfhttps://hdl.handle.net/11000/34947reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheInglés812https://doi.org/10.3390/math8122098info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/349472026-05-27T13:36:21Z
dc.title.none.fl_str_mv Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
title Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
spellingShingle Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
González Espinosa, Martín
multilevel simultaneous equation model
maximum likelihood estimation
genetic algorithms
optimization
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
title_short Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
title_full Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
title_fullStr Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
title_full_unstemmed Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
title_sort Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms
dc.creator.none.fl_str_mv González Espinosa, Martín
Hernández Sanjaime, Rocío
López-Espín, Jose J.
author González Espinosa, Martín
author_facet González Espinosa, Martín
Hernández Sanjaime, Rocío
López-Espín, Jose J.
author_role author
author2 Hernández Sanjaime, Rocío
López-Espín, Jose J.
author2_role author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Estadística, Matemáticas e Informática
dc.subject.none.fl_str_mv multilevel simultaneous equation model
maximum likelihood estimation
genetic algorithms
optimization
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
topic multilevel simultaneous equation model
maximum likelihood estimation
genetic algorithms
optimization
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
description Problems in estimating simultaneous equation models when error terms are not intertemporally uncorrelated has motivated the introduction of a new multivariate model referred to as Multilevel Simultaneous Equation Model (MSEM). The maximum likelihood estimation of the parameters of an MSEM has been set forth. Because of the difficulties associated with the solution of the system of likelihood equations, the maximum likelihood estimator cannot be obtained through exhaustive search procedures. A hybrid metaheuristic that combines a genetic algorithm and an optimization method has been developed to overcome both technical and analytical limitations in the general case when the covariance structure is unknown. The behaviour of the hybrid metaheuristic has been discussed by varying different tuning parameters. A simulation study has been included to evaluate the adequacy of this estimator when error terms are not serially independent. Finally, the performance of this estimation approach has been compared with regard to other alternatives.
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 https://hdl.handle.net/11000/34947
url https://hdl.handle.net/11000/34947
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 8
12
https://doi.org/10.3390/math8122098
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
12
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:REDIUMH. Depósito Digital de la UMH
instname:Universidad Miguel Hernández de Elche
instname_str Universidad Miguel Hernández de Elche
reponame_str REDIUMH. Depósito Digital de la UMH
collection REDIUMH. Depósito Digital de la UMH
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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