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A simultaneous spatial autoregressive model for compositional data

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  • Thi Huong An Nguyen
  • Christine Thomas-Agnan
  • Thibault Laurent
  • Anne Ruiz-Gazen

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.

Suggested Citation

  • Thi Huong An Nguyen & Christine Thomas-Agnan & Thibault Laurent & Anne Ruiz-Gazen, 2021. "A simultaneous spatial autoregressive model for compositional data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(2), pages 161-175, April.
    • Handle: RePEc:taf:specan:v:16:y:2021:i:2:p:161-175
    DOI: 10.1080/17421772.2020.1828613
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    Cited by:

    1. Dargel, Lukas & Thomas-Agnan, Christine, 2023. "Share-ratio interpretations of compositional regression models," TSE Working Papers 23-1456, Toulouse School of Economics (TSE), revised 20 Sep 2023.
    2. Thibault Laurent & Christine Thomas-Agnan & Anne Ruiz-Gazen, 2023. "Covariates impacts in spatial autoregressive models for compositional data," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-23, December.
    3. Woraphon Yamaka & Siritaya Lomwanawong & Darin Magel & Paravee Maneejuk, 2022. "Analysis of the Lockdown Effects on the Economy, Environment, and COVID-19 Spread: Lesson Learnt from a Global Pandemic in 2020," IJERPH, MDPI, vol. 19(19), pages 1-21, October.

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