Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model
Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model
复制标题
空间误差相关模型的 QML 估计量的渐近分布和有限样本偏差校正
DOI：
10.3390/econometrics3020376
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发表时间：
2015
期刊：
影响因子：
1.5
通讯作者：
Zhenlin Yang
中科院分区：
文献类型：
--
作者：
Shew Fan Liu;Zhenlin Yang
在空间线性回归模型的拟极大似然（QML）估计的渐近性质和有限样本性质的研究中，空间滞后相关（SLD）模型受到了较多的关注，而空间误差相关（SED）模型却很少得到研究.特别是，QML估计的收敛速度的空间依赖性的影响还没有正式研究，并没有给出纠正有限样本偏差的QML估计的方法。本文填补了这些空白。在这两者中，偏差校正对该模型的应用特别重要，因为它可能会大大改善回归系数的推断。与通常的看法相反，SED模型的QML估计量的大样本和小样本行为在收敛速度和偏倚幅度方面可能与SLD模型的不同。蒙特卡罗模拟结果表明，这种偏差是严重的，所提出的偏差校正方法是非常有效的。
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as it leads potentially to much improved inferences for the regression coefficients. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe, and the proposed bias correction procedure is very effective.
影响因子：
1.9
作者：
Kelejian, HH;Prucha, IR
通讯作者：
Prucha, IR