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

Osman Doǧan*, Süleyman Taşpinar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalRegional Science and Urban Economics
Volume45
Issue number1
DOIs
Publication statusPublished - Mar 2014
Externally publishedYes

Keywords

  • Asymptotics
  • GMM
  • Markov Chain Monte Carlo (MCMC)
  • MLE
  • Robustness
  • Spatial autoregressive models
  • Unknown heteroskedasticity

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