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Nguyen, Thi Huong An
, Thomas-Agnan, Christine, Laurent, Thibault, Ruiz-Gazen, Anne, Chakir, Raja and Lungarska, Anna
(2021)
A simultaneous spatial autoregressive model for compositional data.
Spatial Economic Analysis, vol. 16 (n° 2).
pp. 161-175.
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Abstract
In an election, the vote shares by party for a given subdivision of a territory form a compositional vector (positive components adding up to 1). Conventional multiple linear regression models are not adapted to explain this composition due to the constraint on the sum of the components and the potential spatial autocorrelation across territorial units. We develop a simultaneous spatial autoregressive model for compositional data that allows for both spatial correlation and correlations across equations. Using simulations and a data set from the 2015 French departmental election, we illustrate its estimation by two-stage and three-stage least squares methods.
| Item Type: | Article |
|---|---|
| Language: | English |
| Date: | June 2021 |
| Refereed: | Yes |
| Uncontrolled Keywords: | Multivariate Spatial Autocorrelation, Spatial Weight Matrix, Three-stage Least Squares, Two-stage, Least, Squares, simplex, electorel data, CoDa |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 11 Dec 2020 13:32 |
| Last Modified: | 04 Sep 2023 08:41 |
| OAI Identifier: | oai:tse-fr.eu:124962 |
| URI: | https://publications.ut-capitole.fr/id/eprint/41981 |
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A simultaneous spatial autoregressive model for compositional data. (deposited 22 Jul 2019 07:42)
- A simultaneous spatial autoregressive model for compositional data. (deposited 11 Dec 2020 13:32) [Currently Displayed]
