Testing for spatial error autocorrelation in the presence of endogenous regressors
Testing for spatial error autocorrelation in the presence of endogenous regressors
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DOI：
10.1177/016001769702000109
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发表时间：
1997-01-01
影响因子：
2.3
通讯作者：
Kelejian, HH
中科院分区：
文献类型：
--
作者：
Anselin, L;Kelejian, HH
本文考察了空间误差自相关的Moran‘s I检验的性质，当内生变量包含在回归规范中，并通过工具变量程序(如两阶段最小二乘法)进行估计时。统计量的渐近分布是在一个包含系统反馈和空间相互作用(以空间滞后因变量的形式)引起的内生性的一般模型中正式推导出来的。在一系列蒙特卡罗模拟实验中评估了该测试的小样本性能，并将该测试与文献中建议的一些特别方法进行了比较。虽然其中一些特别程序的表现出人意料地好，但在存在空间滞后因变量的情况下，新的测试是唯一可以接受的测试。该测试很容易计算，应该成为对横截面数据估计的内生性模型进行常规规范测试的一部分。
This paper examines the properties of Moran's I test for spatial error autocorrelation when endogenous variables are included in the regression specification and estimation is carried out by means of instrumental variables procedures (such as two-stage least squares). The asymptotic distribution of the statistic is formally derived in a general model that encompasses endogeneity due to system feedbacks as well as spatial interaction (in the form of spatially lagged dependent variables). The small sample performance of the test is assessed in a series of Monte Carlo simulation experiments, and the test is compared to a number of ad hoc approaches that have been suggested in the literature. While some of these ad hoc procedures perform surprisingly well, the new test is the only acceptable one in the presence of spatially lagged dependent variables. The test is straightforward to compute and should become part of routine specification testing of models with endogeneity that are estimated for cross-sectional data.