Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability

Simultaneous Equations Model (SEM) is a set of regression equations where bidirectional relationships exist between variables. SEMs are widely used to model complex systems, capture the interdependencies between different variables, and make predictions about future outcomes in a wide range of field...

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Detalles Bibliográficos
Autores: Pérez-Sánchez, Belén, Perea, Carmen, Gonzalez, Martin, 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:11000/38566
Acceso en línea:https://hdl.handle.net/11000/38566
Access Level:acceso abierto
Palabra clave:Simultaneous equation models
Optimized Bayesian method of moments
Entropy
Computational statistics
CDU::0 - Generalidades.
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spelling Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data VariabilityPérez-Sánchez, BelénPerea, CarmenGonzalez, MartinLópez-Espín, Jose J.Simultaneous equation modelsOptimized Bayesian method of momentsEntropyComputational statisticsCDU::0 - Generalidades.Simultaneous Equations Model (SEM) is a set of regression equations where bidirectional relationships exist between variables. SEMs are widely used to model complex systems, capture the interdependencies between different variables, and make predictions about future outcomes in a wide range of fields such as economics, markets, or health sciences. In the literature, the performance of numerous methods, both classical and Bayesian, has been widely studied in various aspects such as endogeneity or correlation. To our knowledge, the study of estimator performance under varying levels of data variability in simultaneous equation models is not well-developed. This paper aims to evaluate the performance of methods for estimating SEMs of different sizes, considering the number of variables and the variability of endogenous variables. An experimental study has been conducted applying different estimation methods, including Two Stage Least Squares (2SLS) and the Optimized Bayesian Method of Moments (BmomOPT ), to evaluate their performance across different SEMs. Based on our computational results, the main finding is that the performance of the methods depends on the variability of the data, with BmomOPT being more accurate at lower levels of variability. These results could interest researchers aiming to apply SEMs in practical cases as they offer insights into selecting the estimation method while considering both the model size and data variabilitySpringerOpenDepartamentos de la UMH::Estadística, Matemáticas e Informática202520252025info:eu-repo/semantics/articleapplication/pdf10application/pdfhttps://hdl.handle.net/11000/38566reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheIngléshttps://doi.org/10.1007/s41019-025-00318-6info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/385662026-05-27T13:36:21Z
dc.title.none.fl_str_mv Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
title Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
spellingShingle Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
Pérez-Sánchez, Belén
Simultaneous equation models
Optimized Bayesian method of moments
Entropy
Computational statistics
CDU::0 - Generalidades.
title_short Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
title_full Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
title_fullStr Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
title_full_unstemmed Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
title_sort Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability
dc.creator.none.fl_str_mv Pérez-Sánchez, Belén
Perea, Carmen
Gonzalez, Martin
López-Espín, Jose J.
author Pérez-Sánchez, Belén
author_facet Pérez-Sánchez, Belén
Perea, Carmen
Gonzalez, Martin
López-Espín, Jose J.
author_role author
author2 Perea, Carmen
Gonzalez, Martin
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 equation models
Optimized Bayesian method of moments
Entropy
Computational statistics
CDU::0 - Generalidades.
topic Simultaneous equation models
Optimized Bayesian method of moments
Entropy
Computational statistics
CDU::0 - Generalidades.
description Simultaneous Equations Model (SEM) is a set of regression equations where bidirectional relationships exist between variables. SEMs are widely used to model complex systems, capture the interdependencies between different variables, and make predictions about future outcomes in a wide range of fields such as economics, markets, or health sciences. In the literature, the performance of numerous methods, both classical and Bayesian, has been widely studied in various aspects such as endogeneity or correlation. To our knowledge, the study of estimator performance under varying levels of data variability in simultaneous equation models is not well-developed. This paper aims to evaluate the performance of methods for estimating SEMs of different sizes, considering the number of variables and the variability of endogenous variables. An experimental study has been conducted applying different estimation methods, including Two Stage Least Squares (2SLS) and the Optimized Bayesian Method of Moments (BmomOPT ), to evaluate their performance across different SEMs. Based on our computational results, the main finding is that the performance of the methods depends on the variability of the data, with BmomOPT being more accurate at lower levels of variability. These results could interest researchers aiming to apply SEMs in practical cases as they offer insights into selecting the estimation method while considering both the model size and data variability
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/11000/38566
url https://hdl.handle.net/11000/38566
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1007/s41019-025-00318-6
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
10
application/pdf
dc.publisher.none.fl_str_mv SpringerOpen
publisher.none.fl_str_mv SpringerOpen
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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