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摘要翻译：
如何确定社区一级的治疗，如引入社会计划或贸易冲击，是否会改变代理人在网络中形成联系的动机？本文提出了两样本Kolmogorov-Smirnov检验的类似物，该检验在文献中广泛用于检验网络数据的“无处理效应”的无效假设。它首先给出了一个测试问题，其中零假设是从同一个随机图模型中提取两个网络。然后描述了两个基于网络邻接矩阵之间的差值的随机化检验，这两个差值是由$2~2$和$infty~1$算子范数度量的。测试的功率特性分析，在模拟中，并通过两个现实世界的应用。一个关键的发现是，对于经济学中常见的稀疏网络和度异构网络，基于$infty\to1$范数的检验比基于$2\to2$范数的检验要强得多。
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英文标题：
《Testing for Differences in Stochastic Network Structure》
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作者：
Eric Auerbach
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
How can one determine whether a community-level treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogues of a two-sample Kolmogorov-Smirnov test, widely used in the literature to test the null hypothesis of "no treatment effects", for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes two randomization tests based on the magnitude of the difference between the networks' adjacency matrices as measured by the $2\to2$ and $\infty\to1$ operator norms. Power properties of the tests are examined analytically, in simulation, and through two real-world applications. A key finding is that the test based on the $\infty\to1$ norm can be substantially more powerful than that based on the $2\to2$ norm for the kinds of sparse and degree-heterogeneous networks common in economics.
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PDF链接：
https://arxiv.org/pdf/1903.11117
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