This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.

中文翻译：

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空间自相关计数的建模和诊断

本文提出了一种新的空间滞后回归模型，该模型解决了由计数之间的横截面依赖性引起的全局空间自相关。我们的方法提供了空间相关参数的直观解释，作为相邻观测对计数条件期望影响的测量。它允许使用标准数值程序基于不同的分布假设进行灵活的基于似然的推理。此外，我们提倡在空间计数回归模型中使用数据一致的诊断工具。该应用程序重新访问了有关美国制造业中单体初创公司位置选择的数据集。