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A Spatial Durbin Model for Compositional Data

In: Advances in Contemporary Statistics and Econometrics

Author

Listed:
  • Tingting Huang

    (Capital University of Economics and Business, School of Statistics
    Beihang University, School of Economics and Management
    Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations)

  • Gilbert Saporta

    (CNAM, Center for Studies and Research in Computer Science and Communication)

  • Huiwen Wang

    (Beihang University, School of Economics and Management
    Beihang University, Beijing Advanced Innovation Center for Big Data and Brain Computing)

Abstract

A compositional linear model (regression of a scalar response on a set of compositions) for areal data is proposed, where observations are not independent and present spatial autocorrelation. Specifically, we borrow thoughts from the spatial Durbin model considering that it produces unbiased coefficient estimates compared to other spatial linear regression models (including the spatial error model, the spatial autoregressive model, the Kelejian-Prucha model, and the spatial Durbin error model). The orthonormal log-ratio (olr) transformation based on a sequential binary partition of compositions and maximum likelihood estimation method are employed to estimate the new model. We also check the proposed estimators on a simulated and a real dataset.

Suggested Citation

  • Tingting Huang & Gilbert Saporta & Huiwen Wang, 2021. "A Spatial Durbin Model for Compositional Data," Springer Books, in: Abdelaati Daouia & Anne Ruiz-Gazen (ed.), Advances in Contemporary Statistics and Econometrics, pages 471-488, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-73249-3_24
    DOI: 10.1007/978-3-030-73249-3_24
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