摘要：The ordinary least squares (OLS) estimator for spatial autoregressions may be consistentbr /as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significantbr ortion of the total units. This paper presents a unified asymptotic distribution result of the properlybr /recentered OLS estimator and proposes a new estimator that is based on the indirect inferencebr /(II) procedure. The resulting estimator can always be used regardless of the degree of aggregatebr /influence on each spatial unit from other units and is consistent and asymptotically normal. The newbr /estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity.br /Its good finite-sample performance, in comparison with existing estimators that are also robust tobr /heteroscedasticity, is demonstrated by a Monte Carlo study.
