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Advances in Contemporary Statistics and Econometrics pp 471–488Cite as

A Spatial Durbin Model for Compositional Data

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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.

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Acknowledgements

This research was financially supported by the National Natural Science Foundation of China under Grant No. 71420107025 and Capital University of Economics and Business under Grant No. XRZ2021040.

Author information

Authors and Affiliations

  1. School of Statistics, Capital University of Economics and Business, Beijing, China

    Tingting Huang

  2. School of Economics and Management, Beihang University, Beijing, China

    Tingting Huang & Huiwen Wang

  3. Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations, Beijing, China

    Tingting Huang

  4. CNAM, Center for Studies and Research in Computer Science and Communication, Paris, France

    Gilbert Saporta

  5. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China

    Huiwen Wang

Authors
  1. Tingting Huang
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  2. Gilbert Saporta
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  3. Huiwen Wang
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Corresponding author

Correspondence to Gilbert Saporta .

Editor information

Editors and Affiliations

  1. Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France

    Abdelaati Daouia

  2. Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France

    Anne Ruiz-Gazen

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Huang, T., Saporta, G., Wang, H. (2021). A Spatial Durbin Model for Compositional Data. In: Daouia, A., Ruiz-Gazen, A. (eds) Advances in Contemporary Statistics and Econometrics. Springer, Cham. https://doi.org/10.1007/978-3-030-73249-3_24

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