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A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix

The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so-called [Formula: see text] issue. Probably, the aprioristic approach is not the best solution although, presently, there are few alternatives for the use...

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Autores principales: Herrera, Marcos, Mur, Jesus, Ruiz, Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514642/
https://www.ncbi.nlm.nih.gov/pubmed/33266875
http://dx.doi.org/10.3390/e21020160
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author Herrera, Marcos
Mur, Jesus
Ruiz, Manuel
author_facet Herrera, Marcos
Mur, Jesus
Ruiz, Manuel
author_sort Herrera, Marcos
collection PubMed
description The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so-called [Formula: see text] issue. Probably, the aprioristic approach is not the best solution although, presently, there are few alternatives for the user. Our contribution focuses on the problem of selecting a [Formula: see text] matrix from among a finite set of matrices, all of them considered appropriate for the case. We develop a new and simple method based on the entropy corresponding to the distribution of probability estimated for the data. Other alternatives, which are common in current applied work, are also reviewed. The paper includes a large study of Monte Carlo to calibrate the effectiveness of our approach compared to others. A well-known case study is also included.
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spelling pubmed-75146422020-11-09 A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix Herrera, Marcos Mur, Jesus Ruiz, Manuel Entropy (Basel) Article The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so-called [Formula: see text] issue. Probably, the aprioristic approach is not the best solution although, presently, there are few alternatives for the user. Our contribution focuses on the problem of selecting a [Formula: see text] matrix from among a finite set of matrices, all of them considered appropriate for the case. We develop a new and simple method based on the entropy corresponding to the distribution of probability estimated for the data. Other alternatives, which are common in current applied work, are also reviewed. The paper includes a large study of Monte Carlo to calibrate the effectiveness of our approach compared to others. A well-known case study is also included. MDPI 2019-02-08 /pmc/articles/PMC7514642/ /pubmed/33266875 http://dx.doi.org/10.3390/e21020160 Text en © 2019 by the authors. 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/).
spellingShingle Article
Herrera, Marcos
Mur, Jesus
Ruiz, Manuel
A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
title A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
title_full A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
title_fullStr A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
title_full_unstemmed A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
title_short A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
title_sort comparison study on criteria to select the most adequate weighting matrix
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514642/
https://www.ncbi.nlm.nih.gov/pubmed/33266875
http://dx.doi.org/10.3390/e21020160
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