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Heterogeneous regression models for clusters of spatial dependent data

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  • Zhihua Ma
  • Yishu Xue
  • Guanyu Hu

Abstract

In economic development there are often regions that share similar socioeconomic characteristics, and econometrics models on such regions tend to produce similar covariate effect estimates. This paper proposes a Bayesian clustered regression for spatially dependent data in order to detect clusters in covariate effects. The proposed method is based on the Dirichlet process, which provides a probabilistic framework for simultaneous inference of the number of clusters and clustering configurations. The use of the method is illustrated both in simulation studies and by an application to a housing cost data set of Georgia.

Suggested Citation

  • Zhihua Ma & Yishu Xue & Guanyu Hu, 2020. "Heterogeneous regression models for clusters of spatial dependent data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 15(4), pages 459-475, October.
    • Handle: RePEc:taf:specan:v:15:y:2020:i:4:p:459-475
    DOI: 10.1080/17421772.2020.1784989
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    Cited by:

    1. Wang, Xin & Zhu, Zhengyuan & Zhang, Hao Helen, 2023. "Spatial heterogeneity automatic detection and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    3. Lijiang Geng & Guanyu Hu, 2022. "Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data," Biometrics, The International Biometric Society, vol. 78(2), pages 536-547, June.
    4. Luc Anselin & Pedro Amaral, 2024. "Endogenous spatial regimes," Journal of Geographical Systems, Springer, vol. 26(2), pages 209-234, April.
    5. Lin, Fangzheng & Tang, Yanlin & Zhu, Huichen & Zhu, Zhongyi, 2022. "Spatially clustered varying coefficient model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

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