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A simultaneous spatial autoregressive model for compositional data

Author

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  • Nguyen, T.H.A
  • Thomas-Agnan, Christine
  • Laurent, Thibault
  • Ruiz-Gazen, Anne

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.

Suggested Citation

  • Nguyen, T.H.A & Thomas-Agnan, Christine & Laurent, Thibault & Ruiz-Gazen, Anne, 2019. "A simultaneous spatial autoregressive model for compositional data," TSE Working Papers 19-1028, Toulouse School of Economics (TSE), revised Apr 2020.
    • Handle: RePEc:tse:wpaper:123213
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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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