Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures.

中文翻译：

---

在空间自回归模型中测试影响度量

研究人员经常利用线性回归模型来评估政策对目标结果的影响。在正确指定的线性回归模型中，仅通过线性回归系数来衡量边际影响。但是，在处理同步和历时空间数据时，参数的解释会更加复杂，因为策略的影响会扩展到相邻位置。文献中提出了针对横截面空间线性回归模型和空间面板数据模型的总结措施。在本文中，我们比较了三种方法来测试空间线性回归模型中影响度量的重要性。这些过程包括（i）估计方程法，（ii）经典增量法和（iii）模拟法。