X Marks the Spot: Unlocking the Treasure of Spatial-X Models
X Marks the Spot: Unlocking the Treasure of Spatial-X Models
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X 标志着这一点：解锁 Spatial-X 模型的宝藏
DOI：
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发表时间：
2020
影响因子：
3.1
通讯作者：
Laron K. Williams
中科院分区：
文献类型：
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作者：
Cameron Wimpy;G. Whitten;Laron K. Williams
近年来，政治学家广泛使用空间计量经济学模型来检验各种理论。在回顾空间论文，我们发现，大多数这些研究使用的空间自回归（SAR）模型。虽然这是一个强大的方法，揭示了扩散过程的推论，它也是高度限制性的，并作出假设，往往是不适当的表达理论。我们认为，空间-X（SLX）模型是一个更好地反映了典型的理论空间过程。我们的模拟表明，SLX模型一致检索协变量的直接和间接影响时，真实的数据生成过程反映其他空间过程。然而，SAR模型往往会发现数据中不存在的幻影高阶效应。我们进一步展示了SLX模型如何揭示SAR模型无法发现的国家国防负担空间依赖模式的异质性。
In recent years, political scientists have made extensive use of spatial econometric models to test a wide range of theories. In a review of spatial papers, we find that a majority of these studies use the spatial autoregressive (SAR) model. Although this is a powerful method that reveals inferences about diffusion processes, it is also highly restrictive and makes assumptions that often are not appropriate given the expressed theories. We contend that spatial-X (SLX) models are a better reflection of typical theories about spatial processes. Our simulations demonstrate that SLX models consistently retrieve the direct and indirect effects of covariates when the true data-generating process reflects other spatial processes. SAR models, however, tend to find phantom higher-order effects that are not present in the data. We further demonstrate how SLX models reveal heterogeneity in patterns of spatial dependence in countries’ defense burdens that SAR models cannot discover.