揭示的社会网络
Revealed Social Networks
AI总结：
本文针对线性均值模型开发显示偏好式检验，研究其识别特性，发现结果变量维度与识别密切相关，一维时识别失效普遍，多维时识别普遍存在。
摘要：
线性均值模型是 peer effects 的标准实证模型，它要求个体的选择或结果是其理想点与所在群体平均结果的结合。我们利用选择数据和外生群体变异，开发了一种针对线性均值模型的显示偏好式检验方法，该检验被构建为一个线性规划，可被理解为一致区分每个个体行为的条件。随后我们研究了线性均值模型的识别特性，分析的关键结论是结果变量的维度与识别之间存在密切关系：当结果变量为一维时，识别失效是普遍存在的；当结果变量为多维时，我们给出了识别普遍存在的自然条件。
英文摘要：
The linear-in-means model is the standard empirical model of peer effects and asks that an agent's choice or outcome is a combination of their ideal point and the mean outcome of their group. Using choice data and exogenous group variation, we develop a revealed preference style test for the linear-in-means model. This test is formulated as a linear program and can be interpreted as a condition about differentiating the behavior of each agent in a consistent manner. We then study the identification properties of the linear-in-means model. A key takeaway from our analysis is the close relationship between the dimension of the outcome variable and identification. When the outcome variable is one-dimensional, failures of identification are generic. When the outcome variable is multi-dimensional, we provide natural conditions under which identification is generic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
