Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

Süleyman Taşpınar, Osman Doğan, Anil K. Bera

Research output: Contribution to journalArticlepeer-review


In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.

Original languageEnglish (US)
Pages (from-to)241-268
Number of pages28
JournalSpatial Economic Analysis
Issue number2
StatePublished - Apr 3 2019


  • asymptotic variance
  • efficiency
  • generalized method of moments (GMM)
  • heteroskedasticity
  • heteroskedasticity-consistent covariance matrix estimator (HCCME)
  • inference
  • spatial autoregressive models
  • standard errors

ASJC Scopus subject areas

  • Geography, Planning and Development
  • General Economics, Econometrics and Finance
  • Statistics, Probability and Uncertainty
  • Earth and Planetary Sciences (miscellaneous)


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