GMM estimation of spatial autoregressive models with unknown heteroskedasticity

被引:168
|
作者
Lin, Xu [2 ]
Lee, Lung-fei [1 ]
机构
[1] Ohio State Univ, Dept Econ, Columbus, OH 43210 USA
[2] Wayne State Univ, Dept Econ, Detroit, MI 48202 USA
基金
美国国家科学基金会;
关键词
Spatial autoregression; Unknown heteroskedasticity; Robustness; Consistent covariance matrix; GMM; NEIGHBORHOOD;
D O I
10.1016/j.jeconom.2009.10.035
中图分类号
F [经济];
学科分类号
02 ;
摘要
In the presence of heteroskedastic disturbances, the MLE for the SAR models without taking into account the heteroskedasticity is generally inconsistent. The 2SLS estimates can have large variances and biases for cases where regressors do not have strong effects. In contrast, GMM estimators obtained from certain moment conditions can be robust. Asymptotically valid inferences can be drawn with consistently estimated covariance matrices. Efficiency can be improved by constructing the optimal weighted estimation. The approaches are applied to the study of county teenage pregnancy rates. The empirical results show a strong spatial convergence among county teenage pregnancy rates. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:34 / 52
页数:19
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