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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-7514642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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