Computational Issues in the Estimation of the Spatial Probit Model: A Comparison of Various Estimators.
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| Title: | Computational Issues in the Estimation of the Spatial Probit Model: A Comparison of Various Estimators. |
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| Authors: | Billé, Anna Gloria1 ag.bille@unich.it |
| Source: | Review of Regional Studies. 2013, Vol. 43 Issue 2/3, p131-152. 22p. 1 Diagram, 6 Charts, 2 Graphs. |
| Subject Terms: | *Algorithms, Sparse matrices, Social aspects, Mathematical statistics, Parameter estimation, Quantitative research |
| Abstract: | In spatial discrete choice models the spatial dependent structure adds complexity in the estimation of parameters. Appropriate general method of moments (GMM) estimation needs inverses of n-by-n matrices and an optimization complexity of the moment conditions for moderate to large samples makes practical applications more difficult. Recently, Klier and McMillen (2008) have proposed a linearized version of the GMM estimator that avoids the infeasible problem of inverting n-by-n matrices when employing large samples. They show that standard GMM reduces to a nonlinear two-stage least squares problem. On the other hand, when we deal with full maximum likelihood (FML) estimation, a multidimensional integration problem arises and a viable computational solution needs to be found. Although it remains somewhat computationally burdensome, since the inverses of matrices dimensioned by the number of observations have to be computed, the ML estimator yields the potential advantage of efficiency. Therefore, through Monte Carlo experiments we compare GMM-based approaches with ML estimation in terms of their computation times and statistical properties. Furthermore, a comparison in terms of the marginal effects also is included. Finally, we recommend an algorithm based on sparse matrices that enables more efficient use of both ML and GMM estimators. [ABSTRACT FROM AUTHOR] |
| Copyright of Review of Regional Studies is the property of Southern Regional Science Association Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 95605134 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Computational Issues in the Estimation of the Spatial Probit Model: A Comparison of Various Estimators. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Billé%2C+Anna+Gloria%22">Billé, Anna Gloria</searchLink><relatesTo>1</relatesTo><i> ag.bille@unich.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Review+of+Regional+Studies%22">Review of Regional Studies</searchLink>. 2013, Vol. 43 Issue 2/3, p131-152. 22p. 1 Diagram, 6 Charts, 2 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrices%22">Sparse matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Social+aspects%22">Social aspects</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In spatial discrete choice models the spatial dependent structure adds complexity in the estimation of parameters. Appropriate general method of moments (GMM) estimation needs inverses of n-by-n matrices and an optimization complexity of the moment conditions for moderate to large samples makes practical applications more difficult. Recently, Klier and McMillen (2008) have proposed a linearized version of the GMM estimator that avoids the infeasible problem of inverting n-by-n matrices when employing large samples. They show that standard GMM reduces to a nonlinear two-stage least squares problem. On the other hand, when we deal with full maximum likelihood (FML) estimation, a multidimensional integration problem arises and a viable computational solution needs to be found. Although it remains somewhat computationally burdensome, since the inverses of matrices dimensioned by the number of observations have to be computed, the ML estimator yields the potential advantage of efficiency. Therefore, through Monte Carlo experiments we compare GMM-based approaches with ML estimation in terms of their computation times and statistical properties. Furthermore, a comparison in terms of the marginal effects also is included. Finally, we recommend an algorithm based on sparse matrices that enables more efficient use of both ML and GMM estimators. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Review of Regional Studies is the property of Southern Regional Science Association Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.52324/001c.8088 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 131 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Sparse matrices Type: general – SubjectFull: Social aspects Type: general – SubjectFull: Mathematical statistics Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Quantitative research Type: general Titles: – TitleFull: Computational Issues in the Estimation of the Spatial Probit Model: A Comparison of Various Estimators. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Billé, Anna Gloria IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 0048749X Numbering: – Type: volume Value: 43 – Type: issue Value: 2/3 Titles: – TitleFull: Review of Regional Studies Type: main |
| ResultId | 1 |
