A simultaneous spatial autoregressive model for compositional data

T.H.a Nguyen, Christine Thomas-Agnan, Thibault Laurent and Anne Ruiz-Gazen

No 19-1028, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: 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 existence of the constraint on the sum of the components and the assumption of statistical independence across territorial units 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.

Keywords: multivariate spatial autocorrelation; spatial weight matrix; three-stage least squares; two-stage least squares; simplex; electoral data; CoDa. (search for similar items in EconPapers)
Date: 2019-07, Revised 2020-04
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: A simultaneous spatial autoregressive model for compositional data (2021)
Working Paper: A simultaneous spatial autoregressive model for compositional data (2021)
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