Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Taşpınar, Süleyman; Doğan, Osman; Bera, Anil K.
• Is Part Of: Spatial economic analysis. Volume 14:Issue 2(2019)
• Page Start: 241
• Page End: 268
• Publication Date: 2019-04-03
• Subjects: spatial autoregressive models -- generalized method of moments (GMM) -- heteroskedasticity -- heteroskedasticity-consistent covariance matrix estimator (HCCME) -- asymptotic variance -- efficiency -- standard errors -- inference; C13 -- C21 -- C31; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2019.1549366
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: 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.