Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent

Simultaneous equation model (SEM) is an econometric technique traditionally used in economics but with many applications in other sciences. This model allows the bidirectional relationship between variables and a simultaneous relationship between the equation set. There are many estimators used for...

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Autores: Pérez-Sánchez, Belén, Perea, Carmen, Duran Ballester, Guillem, López-Espín, Jose J.
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/38567
Acceso en línea:https://hdl.handle.net/11000/38567
Access Level:acceso abierto
Palabra clave:Backpropagation method
Stochastic gradient descent
Simultaneous equation models
Artificial neural networks
CDU::0 - Generalidades.
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spelling Estimation of simultaneous equation models by backpropagation method using stochastic gradient descentPérez-Sánchez, BelénPerea, CarmenDuran Ballester, GuillemLópez-Espín, Jose J.Backpropagation methodStochastic gradient descentSimultaneous equation modelsArtificial neural networksCDU::0 - Generalidades.Simultaneous equation model (SEM) is an econometric technique traditionally used in economics but with many applications in other sciences. This model allows the bidirectional relationship between variables and a simultaneous relationship between the equation set. There are many estimators used for solving an SEM. Two-steps least squares (2SLS), three-steps least squares (3SLS), indirect least squares (ILS), etc. are some of the most used of them. These estimators let us obtain a value of the coefficient of an SEM showing the relationship between the variables. There are different works to study and compare the estimators of an SEM comparing the error in the prediction of the data, the computational cost, etc. Some of these works study the estimators from different paradigms such as classical statistics, Bayesian statistics, non-linear regression models, etc. This work proposes to assume an SEM as a particular case of an artificial neural networks (ANN), considering the neurons of the ANN as the variables of the SEM and the weight of the connections of the neurons the coefficients of the SEM. Thus, backpropagation method using stochastic gradient descent (SGD) is proposed and studied as a new method to obtain the coefficient of an SEM.PeerJDepartamentos de la UMH::Estadística, Matemáticas e Informática202520252025info:eu-repo/semantics/articleapplication/pdf14application/pdfhttps://hdl.handle.net/11000/38567reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheIngléshttp://doi.org/10.7717/peerj-cs.2352info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/385672026-05-27T13:36:21Z
dc.title.none.fl_str_mv Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
title Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
spellingShingle Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
Pérez-Sánchez, Belén
Backpropagation method
Stochastic gradient descent
Simultaneous equation models
Artificial neural networks
CDU::0 - Generalidades.
title_short Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
title_full Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
title_fullStr Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
title_full_unstemmed Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
title_sort Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent
dc.creator.none.fl_str_mv Pérez-Sánchez, Belén
Perea, Carmen
Duran Ballester, Guillem
López-Espín, Jose J.
author Pérez-Sánchez, Belén
author_facet Pérez-Sánchez, Belén
Perea, Carmen
Duran Ballester, Guillem
López-Espín, Jose J.
author_role author
author2 Perea, Carmen
Duran Ballester, Guillem
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 Backpropagation method
Stochastic gradient descent
Simultaneous equation models
Artificial neural networks
CDU::0 - Generalidades.
topic Backpropagation method
Stochastic gradient descent
Simultaneous equation models
Artificial neural networks
CDU::0 - Generalidades.
description Simultaneous equation model (SEM) is an econometric technique traditionally used in economics but with many applications in other sciences. This model allows the bidirectional relationship between variables and a simultaneous relationship between the equation set. There are many estimators used for solving an SEM. Two-steps least squares (2SLS), three-steps least squares (3SLS), indirect least squares (ILS), etc. are some of the most used of them. These estimators let us obtain a value of the coefficient of an SEM showing the relationship between the variables. There are different works to study and compare the estimators of an SEM comparing the error in the prediction of the data, the computational cost, etc. Some of these works study the estimators from different paradigms such as classical statistics, Bayesian statistics, non-linear regression models, etc. This work proposes to assume an SEM as a particular case of an artificial neural networks (ANN), considering the neurons of the ANN as the variables of the SEM and the weight of the connections of the neurons the coefficients of the SEM. Thus, backpropagation method using stochastic gradient descent (SGD) is proposed and studied as a new method to obtain the coefficient of an SEM.
publishDate 2025
dc.date.none.fl_str_mv 2025
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/11000/38567
url https://hdl.handle.net/11000/38567
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://doi.org/10.7717/peerj-cs.2352
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
14
application/pdf
dc.publisher.none.fl_str_mv PeerJ
publisher.none.fl_str_mv PeerJ
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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