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A simultaneous spatial autoregressive model for compositional data

Author

Listed:
  • Thi-Huong-An Nguyen

    (Unknown)

  • Christine Thomas-Agnan

    (Unknown)

  • Thibault Laurent

    (Unknown)

  • Anne Ruiz-Gazen

    (Unknown)

  • Chakir Raja

    (Unknown)

  • Anna Lungarska

    (Unknown)

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 & Chakir Raja & Anna Lungarska, 2021. "A simultaneous spatial autoregressive model for compositional data," Post-Print hal-03239250, HAL.
    • Handle: RePEc:hal:journl:hal-03239250
    DOI: 10.1080/17421772.2020.1828613
    as

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