The significance and influence of US Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology -- the citation exponential random graph model, for which we provide user-friendly software -- enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citation should incorporate both the effects of case characteristics and the structure of past citations.

中文翻译：

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最高法院引证的生成动态：使用新的统计网络模型进行分析

美国最高法院多数意见的重要性和影响力很大程度上来自于意见作为未来意见先例的作用。越来越多的文献试图通过研究最高法院的案例引用方式来理解是什么促使以观点为先例。我们提出了有关最高法院引证的现有工作的两个限制。首先，二元引用通常在分析之前汇总到案例级别。其次，引用被视为独立出现。我们提出了一种方法来研究最高法院意见之间在二进层次上的引用（作为网络），从而克服了这些限制。这种方法-引用指数随机图模型，为此，我们提供了用户友好的软件-使研究人员能够考虑案例特征和复杂的网络依赖形式对引文形成的影响。然后，我们分析了一个网络，其中包括1950年至2015年之间判决的所有最高法院案件。我们找到了依赖程序的证据，包括对等，及物性和受欢迎度。依存关系的影响在统计学上和外在协变量的影响一样显着，这表明最高法院引证的模型应既包括案例特征的影响，也应包括过去引证的结构。可传递性和受欢迎度。依存关系的影响在统计学上和外在协变量的影响一样重要，这表明最高法院引文的模型应既包括案例特征的影响，又应包括过去引文的结构。可传递性和受欢迎度。依存关系的影响在统计学上和外在协变量的影响一样显着，这表明最高法院引证的模型应既包括案例特征的影响，也应包括过去引证的结构。