Abstract
We use a particular quasi-generalized least squares (QGLS) approach to study a linear regression model with spatially correlated error terms. The QGLS estimator is consistent, asymptotically normal, computationally easier than GLS, and it appears to not lose much efficiency. A variance–covariance estimator for QGLS, which is robust to heteroskedasticity, spatial correlation and general variance–covariance misspecification is provided.
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