QML estimation of dynamic panel data models with spatial errors
QML estimation of dynamic panel data models with spatial errors
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DOI：
10.1016/j.jeconom.2014.11.002
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发表时间：
2015-03
影响因子：
6.3
通讯作者：
Liangjun Su;Zhenlin Yang
中科院分区：
文献类型：
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作者：
Liangjun Su;Zhenlin Yang
当横截面维度 n 很大并且时间维度 T 固定时，我们提出了具有空间误差的动态面板模型的准最大似然（QML）估计。我们考虑随机效应和固定效应模型，并证明一致性并推导在初始观测的不同假设下 QML 估计量的极限分布。我们提出了一种基于残差的引导方法来估计 QML 估计器的标准误差。蒙特卡洛模拟表明，在初始观测的正确假设下，QML 估计量和引导标准误差在有限样本中表现良好，但在不满足该假设时可能表现不佳。
We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when this assumption is not met.