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摘要翻译：
我们引入了一个测试Agent对网络结构的偏好是否相互依赖的方法。相互依赖的偏好会导致策略行为，因为由agent$I$指导的最优链路集会随着其他agent指导的链路配置而变化。我们的模型还结合了agent特定的进出程度异构性和可观察agent属性的同构性。这将引入$2n+k^2$nuisance参数($n$是网络中的代理数，$k$是可能的代理属性配置数）。在零平衡点下是唯一的，但我们的假设仍然是一个复合的假设，因为非均匀度和同向干扰参数可以在它们的参数空间中自由变化。在替代方案下，我们的模型是不完全的；可能有多个平衡网络配置，我们的测试不知道选择哪一个。出于大小控制的动机，并利用我们的模型\emph{under the null}的指数族结构，我们将自己限制在条件测试中。我们刻画了一类条件检验的精确零分布，并引入了一种新的马尔可夫链蒙特卡罗(MCMC)算法来模拟这种分布。我们还刻画了局部最优测试。这种测试的形式取决于在零邻域中关于策略交互参数的似然梯度。值得注意的是，这种梯度，以及由此产生的局部最佳检验统计量的形式，并不取决于平衡的选择方式。利用这种依赖性的缺乏，我们概述了本地最佳测试的一个可行版本。
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英文标题：
《An optimal test for strategic interaction in social and economic network
formation between heterogeneous agents》
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作者：
Andrin Pelican and Bryan S. Graham
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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：
We introduce a test for whether agents' preferences over network structure are interdependent. Interdependent preferences induce strategic behavior since the optimal set of links directed by agent $i$ will vary with the configuration of links directed by other agents. Our model also incorporates agent-specific in- and out-degree heterogeneity and homophily on observable agent attributes. This introduces $2N+K^2$ nuisance parameters ($N$ is number of agents in the network and $K$ the number of possible agent attribute configurations). Under the null equilibrium is unique, but our hypothesis is nevertheless a composite one as the degree heterogeneity and homophily nuisance parameters may range freely across their parameter space. Under the alternative our model is incomplete; there may be multiple equilibrium network configurations and our test is agnostic about which one is selected. Motivated by size control, and exploiting the exponential family structure of our model \emph{under the null}, we restrict ourselves to conditional tests. We characterize the exact null distribution of a family of conditional tests and introduce a novel Markov Chain Monte Carlo (MCMC) algorithm for simulating this distribution. We also characterize the locally best test. The form of this test depends upon the gradient of the likelihood with respect to the strategic interaction parameter in the neighborhood of the null. Remarkably, this gradient, and consequently the form of the locally best test statistic, does not depend on how an equilibrium is selected. Exploiting this lack of dependence, we outline a feasible version of the locally best test.
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PDF链接：
https://arxiv.org/pdf/2009.00212
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