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

Osman Doǧan*, Süleyman Taşpinar

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13 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1-21
Sayfa sayısı21
DergiRegional Science and Urban Economics
Hacim45
Basın numarası1
DOI'lar
Yayın durumuYayınlandı - Mar 2014
Harici olarak yayınlandıEvet

Finansman

We would like to thank Wim Vijverberg for the insightful and instructive comments on this study. We are also grateful to James P. LeSage for his helpful comments for the improvement of this study. This research was supported, in part, by a grant of computer time from the City University of New York High Performance Computing Center under NSF Grants CNS-0855217 and CNS-0958379.

FinansörlerFinansör numarası
City University of New York High Performance Computing Center
National Science FoundationCNS-0958379, CNS-0855217

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