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Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach

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  • Doğan, Osman
  • Taşpınar, Süleyman

Abstract

Most of the estimators suggested for the estimation of spatial autoregressive models are generally inconsistent in the presence of an unknown form of heteroskedasticity in the disturbance term. The estimators formulated from the generalized method of moments (GMM) and the Bayesian Markov Chain Monte Carlo (MCMC) frameworks can be robust to unknown forms of heteroskedasticity. In this study, the finite sample properties of the robust GMM estimator are compared with the estimators based on the Bayesian MCMC approach for the spatial autoregressive models with heteroskedasticity of an unknown form. A Monte Carlo simulation study provides evaluation of the performance of the heteroskedasticity robust estimators. Our results indicate that the MLE and the Bayesian estimators impose relatively greater bias on the spatial autoregressive parameter when there is negative spatial dependence in the model. In terms of finite sample efficiency, the Bayesian estimators perform better than the robust GMM estimator. In addition, two empirical applications are provided to evaluate relative performance of heteroskedasticity robust estimators.

Suggested Citation

  • Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
  • Handle: RePEc:eee:regeco:v:45:y:2014:i:c:p:1-21
    DOI: 10.1016/j.regsciurbeco.2013.12.003
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    2. Dogan, Osman & Taspinar, Suleyman, 2016. "Bayesian Inference in Spatial Sample Selection Models," MPRA Paper 82829, University Library of Munich, Germany.
    3. Agovino, Massimiliano & Aprile, Maria Carmela & Garofalo, Antonio & Mariani, Angela, 2018. "Cancer mortality rates and spillover effects among different areas: A case study in Campania (southern Italy)," Social Science & Medicine, Elsevier, vol. 204(C), pages 67-83.

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    More about this item

    Keywords

    Spatial autoregressive models; Unknown heteroskedasticity; Robustness; GMM; Asymptotics; MLE; Markov Chain Monte Carlo (MCMC);
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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