A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Abstract: Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimat...

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Detalhes bibliográficos
Autores: Pérez-Sánchez, Belén, González Espinosa, Martín, Perea, Carmen, López-Espín, Jose J.
Formato: artículo
Fecha de publicación:2021
País:España
Recursos:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/34492
Acesso em linha:https://hdl.handle.net/11000/34492
Access Level:acceso abierto
Palavra-chave:simultaneous equations models
bayesian method of moments
markov chain monte carlo
akaike information criteria
entropy
computational statistics
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
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spelling A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter CriteriaPérez-Sánchez, BelénGonzález Espinosa, MartínPerea, CarmenLópez-Espín, Jose J.simultaneous equations modelsbayesian method of momentsmarkov chain monte carloakaike information criteriaentropycomputational statisticsCDU::5 - Ciencias puras y naturales::51 - MatemáticasAbstract: Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.MDPIDepartamentos de la UMH::Estadística, Matemáticas e Informática202520252021info:eu-repo/semantics/articleapplication/pdf9application/pdfhttps://hdl.handle.net/11000/34492reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheInglés9700https://doi.org/10.3390/math9070700info:eu-repo/semantics/openAccessoai:dspace.umh.es:11000/344922026-05-27T13:36:21Z
dc.title.none.fl_str_mv A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
title A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
spellingShingle A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
Pérez-Sánchez, Belén
simultaneous equations models
bayesian method of moments
markov chain monte carlo
akaike information criteria
entropy
computational statistics
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
title_short A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
title_full A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
title_fullStr A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
title_full_unstemmed A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
title_sort A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
dc.creator.none.fl_str_mv Pérez-Sánchez, Belén
González Espinosa, Martín
Perea, Carmen
López-Espín, Jose J.
author Pérez-Sánchez, Belén
author_facet Pérez-Sánchez, Belén
González Espinosa, Martín
Perea, Carmen
López-Espín, Jose J.
author_role author
author2 González Espinosa, Martín
Perea, Carmen
López-Espín, Jose J.
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Estadística, Matemáticas e Informática
dc.subject.none.fl_str_mv simultaneous equations models
bayesian method of moments
markov chain monte carlo
akaike information criteria
entropy
computational statistics
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
topic simultaneous equations models
bayesian method of moments
markov chain monte carlo
akaike information criteria
entropy
computational statistics
CDU::5 - Ciencias puras y naturales::51 - Matemáticas
description Abstract: Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.
publishDate 2021
dc.date.none.fl_str_mv 2021
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/11000/34492
url https://hdl.handle.net/11000/34492
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 9
700
https://doi.org/10.3390/math9070700
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
9
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 15,812429

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