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A simultaneous spatial autoregressive model for compositional data

Nguyen, T.H.A, Thomas-Agnan, Christine, Laurent, Thibault and Ruiz-Gazen, Anne (2019) A simultaneous spatial autoregressive model for compositional data. TSE Working Paper, n. 19-1028, Toulouse

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In an election, the vote shares by party on a given subdivision of a territory form a vector with positive components adding up to 1 called a composition. Using a conventional multiple linear regression model to explain this vector by some factors is not adapted for at least two reasons. The first one is the existence of the constraint on the sum of the components and the second one is the assumption of statistical independence across territorial units which may be questionable due to potential spatial autocorrelation. We develop a simultaneous spatial autoregressive model for compositional data which
allows for both spatial correlation and correlations across equations. We propose an estimation method based on two-stage and three-stage least squares. We illustrate the method with simulations and with a data set from the 2015 French departmental election.

Item Type: Monograph (Working Paper)
Language: English
Date: July 2019
Place of Publication: Toulouse
Uncontrolled Keywords: multivariate spatial autocorrelation, spatial weight matrix, three-stage least squares, two-stage least squares, simplex, electoral data, CoDa.
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 22 Jul 2019 07:42
Last Modified: 14 Apr 2020 12:00
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