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Simple tests for social interaction models with network structures

Author

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  • Osman Doğan
  • Süleyman Taṣpınar
  • Anil K. Bera

Abstract

We consider an extended spatial autoregressive model that can incorporate possible endogenous interactions, exogenous interactions, unobserved group fixed effects and the correlation of unobservables. In the generalized method of moments (GMM) and the maximum likelihood (ML) frameworks, we introduce simple gradient-based robust test statistics that can be used to test for the presence of the endogenous effects, the correlation of unobservables and the contextual effects. These test statistics are robust to local parametric misspecifications and only require consistent estimates from a transformed linear regression model to compute. We carry out an extensive Monte Carlo study to investigate the size and power properties of the proposed tests. The results show that the proposed tests have good finite sample properties, and are useful for testing the presence of the various effects in a social interaction model.

Suggested Citation

  • Osman Doğan & Süleyman Taṣpınar & Anil K. Bera, 2018. "Simple tests for social interaction models with network structures," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(2), pages 212-246, April.
    • Handle: RePEc:taf:specan:v:13:y:2018:i:2:p:212-246
    DOI: 10.1080/17421772.2017.1374550
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    Cited by:

    1. Bera Anil K. & Doğan Osman & Bilias Yannis & Yoon Mann J. & Taşpınar Süleyman, 2020. "Adjustments of Rao’s Score Test for Distributional and Local Parametric Misspecifications," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-29, January.
    2. Bera Anil K. & Doğan Osman & Taşpınar Süleyman, 2019. "Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-33, January.
    3. Jieun Lee, 2022. "Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models," Papers 2209.05563, arXiv.org.
    4. Ye Yang & Osman Dogan & Suleyman Taspinar & Fei Jin, 2023. "A Review of Cross-Sectional Matrix Exponential Spatial Models," Papers 2311.14813, arXiv.org.

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