摘要翻译：
本文提出了一种识别网络中领导者和追随者的新方法。以往的研究使用空间自回归模型(SARs)，隐含地假设网络中的每个个体对他人具有相同的对等效应。机械地，他们认为网络中的关键角色是具有最高中心性的角色。然而，当一些人比其他人更有影响力时，中心性可能不是一个好的衡量标准。我开发了一个模型，考虑到个体特有的内生效应，并提出了一个两阶段套索程序来识别网络中有影响力的个体。在一个稀疏性假设下：只有一个个体子集（随样本容量n增加）有影响，我证明了我的个体特有内生效应的2SLSS估计是一致的，并且达到渐近正态性。我还发展了稳健的推论，包括一致有效的置信区间。这些结果也适用于有影响力的个人并不稀少的情况。我扩展了分析，允许多种类型的连接（多个网络），并展示了如何使用稀疏组套索来检测多种连接类型中哪一种更有影响力。仿真证据表明，我的估计器具有良好的有限样本性能。我进一步将我的方法应用于Banerjee等人的数据。（2013）我提议的程序能够确定领导人和有效的网络。
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英文标题：
《Heterogeneous Endogenous Effects in Networks》
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作者：
Sida Peng
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最新提交年份：
2019
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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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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  This paper proposes a new method to identify leaders and followers in a network. Prior works use spatial autoregression models (SARs) which implicitly assume that each individual in the network has the same peer effects on others. Mechanically, they conclude the key player in the network to be the one with the highest centrality. However, when some individuals are more influential than others, centrality may fail to be a good measure. I develop a model that allows for individual-specific endogenous effects and propose a two-stage LASSO procedure to identify influential individuals in a network. Under an assumption of sparsity: only a subset of individuals (which can increase with sample size n) is influential, I show that my 2SLSS estimator for individual-specific endogenous effects is consistent and achieves asymptotic normality. I also develop robust inference including uniformly valid confidence intervals. These results also carry through to scenarios where the influential individuals are not sparse. I extend the analysis to allow for multiple types of connections (multiple networks), and I show how to use the sparse group LASSO to detect which of the multiple connection types is more influential. Simulation evidence shows that my estimator has good finite sample performance. I further apply my method to the data in Banerjee et al. (2013) and my proposed procedure is able to identify leaders and effective networks.
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PDF链接：
https://arxiv.org/pdf/1908.00663