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GMM estimation of spatial autoregressive models in a system of simultaneous equations with heteroskedasticity


  • Xiaodong Liu
  • Paulo Saraiva


This paper proposes a GMM estimation framework for the SAR model in a system of simultaneous equations with heteroskedastic disturbances. Besides linear moment conditions, the proposed GMM estimator also utilizes quadratic moment conditions based on the covariance structure of model disturbances within and across equations. Compared with the QML approach, the GMM estimator is easier to implement and robust under heteroskedasticity of unknown form. We derive the heteroskedasticity-robust standard error for the GMM estimator. Monte Carlo experiments show that the proposed GMM estimator performs well in finite samples.

Suggested Citation

  • Xiaodong Liu & Paulo Saraiva, 2019. "GMM estimation of spatial autoregressive models in a system of simultaneous equations with heteroskedasticity," Econometric Reviews, Taylor & Francis Journals, vol. 38(4), pages 359-385, April.
  • Handle: RePEc:taf:emetrv:v:38:y:2019:i:4:p:359-385
    DOI: 10.1080/07474938.2017.1308087

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