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Indirect Inference Estimation of Spatial Autoregressions
Econometrics Pub Date : 2020-09-03 , DOI: 10.3390/econometrics8030034
Yong Bao , Xiaotian Liu , Lihong Yang

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

中文翻译:

空间自回归的间接推断估计

Lee(2002)指出,空间自回归的普通最小二乘(OLS)估计可能是一致的,条件是每个空间单位受总单位中很大一部分的影响。本文介绍了经过适当校正的OLS估计量的统一渐近分布结果,并提出了一种基于间接推断(II)程序的新估计量。无论其他单元对每个空间单元的总体影响程度如何,都可以始终使用结果估计器,并且该估计器是一致的且渐近正常的。新的估计器不依赖于分布假设,并且对未知的异方差具有鲁棒性。蒙特卡洛研究表明,与现有的对异方差性强的估计器相比,其良好的有限样本性能。
更新日期:2020-09-03
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