Version 1
: Received: 7 November 2018 / Approved: 8 November 2018 / Online: 8 November 2018 (04:42:15 CET)
How to cite:
Herrera, M.; Mur, J.; Ruiz Marín, M. A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix. Preprints2018, 2018110188. https://doi.org/10.20944/preprints201811.0188.v1
Herrera, M.; Mur, J.; Ruiz Marín, M. A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix. Preprints 2018, 2018110188. https://doi.org/10.20944/preprints201811.0188.v1
Herrera, M.; Mur, J.; Ruiz Marín, M. A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix. Preprints2018, 2018110188. https://doi.org/10.20944/preprints201811.0188.v1
APA Style
Herrera, M., Mur, J., & Ruiz Marín, M. (2018). A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix. Preprints. https://doi.org/10.20944/preprints201811.0188.v1
Chicago/Turabian Style
Herrera, M., Jesús Mur and Manuel Ruiz Marín. 2018 "A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix" Preprints. https://doi.org/10.20944/preprints201811.0188.v1
Abstract
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 $\mathbf{W}$ issue. Probably, the aprioristic approach is not the best solution although, nowadays, there few alternatives for the user. Our contribution focuses on the problem of selecting a $\mathbf{W}$ matrix from among a finite set of matrices, all of them considerer 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 Monte Carlo to calibrate the effectiveness of our approach compared to the others. A well-known case study is also included.
Keywords
weights matrix; model selection; entropy; monte carlo
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
