摘要：This paper proposes and studies two Huber-type estimation approaches, namely, the Huber IV estimation and the Huber GMM estimation, for a spatial autoregressive (SAR) model. We establish the consistency, asymptotic distributions, finite sample breakdown points, and influences of these estimators. Simulation studies show that compared to the corresponding traditional estimators (the two-stage least squares estimator, the best IV estimator, and the GMM estimator), our estimators are more robust when the unknown disturbances are long-tailed, and our estimators only lose a little efficiency when the disturbances are short-tailed. And the Huber GMM estimator also outperforms several robust estimators in the literature. Finally, we apply our estimation method to investigate the impact of the urban heat island effect on housing prices.