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Separable spatial modeling of spillovers and disturbances

机译:溢出和扰动的独立空间建模

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The single spatial parameter in the spatial autoregressive model affects both the estimation of spillovers and the estimation of spatial disturbances. Consequently, the spatial autoregressive model has the undesirable property that if the degree of spatial dependence in the disturbances differs from that in the spillovers, neither may be estimated correctly. We show theoretically that the dependence structure for the spillovers and disturbances can differ and conduct a Monte Carlo experiment that verifies these findings. In contrast, estimates from a simple separable model show little bias in all the scenarios. We also show differences between the spatial autoregressive model and the separable model on five empirical examples.
机译:空间自回归模型中的单个空间参数会影响溢出估计和空间扰动估计。因此,空间自回归模型具有不希望的特性,即如果扰动中的空间依赖性程度与溢出中的空间依赖性程度不同,则两者均不能正确估计。我们从理论上表明,溢出和干扰的依赖结构可能有所不同,并进行了蒙特卡洛实验,以验证这些发现。相比之下,来自简单可分离模型的估计在所有情况下均显示出很小的偏差。我们还通过五个经验示例显示了空间自回归模型与可分离模型之间的差异。

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