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Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm
The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. T...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064307/ https://www.ncbi.nlm.nih.gov/pubmed/33805175 http://dx.doi.org/10.3390/e23040384 |
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author | Hernández-Sanjaime, Rocío González, Martín Peñalver, Antonio López-Espín, Jose J. |
author_facet | Hernández-Sanjaime, Rocío González, Martín Peñalver, Antonio López-Espín, Jose J. |
author_sort | Hernández-Sanjaime, Rocío |
collection | PubMed |
description | The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem. |
format | Online Article Text |
id | pubmed-8064307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80643072021-04-24 Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm Hernández-Sanjaime, Rocío González, Martín Peñalver, Antonio López-Espín, Jose J. Entropy (Basel) Article The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem. MDPI 2021-03-24 /pmc/articles/PMC8064307/ /pubmed/33805175 http://dx.doi.org/10.3390/e23040384 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Hernández-Sanjaime, Rocío González, Martín Peñalver, Antonio López-Espín, Jose J. Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm |
title | Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm |
title_full | Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm |
title_fullStr | Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm |
title_full_unstemmed | Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm |
title_short | Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm |
title_sort | estimating simultaneous equation models through an entropy-based incremental variational bayes learning algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064307/ https://www.ncbi.nlm.nih.gov/pubmed/33805175 http://dx.doi.org/10.3390/e23040384 |
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