在没有网络数据的情况下,利用聚合关系数据可行地识别网络结构。

IF 10.5 1区 经济学 Q1 ECONOMICS American Economic Review Pub Date : 2020-08-01 DOI:10.1257/aer.20170861
Emily Breza, Arun G Chandrasekhar, Tyler H McCormick, Mengjie Pan
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引用次数: 95

摘要

社交网络数据的收集成本往往高得令人望而却步,限制了实证网络研究。我们提出了一种使用聚合关系数据(ARD)进行网络启发的廉价可行的策略:对“你的链接中有多少具有特征k?”形式的问题的回答。我们的方法使用ARD来恢复网络形成模型的参数,该模型允许从节点或图级统计的分布中进行采样。我们复制了使用网络数据的两个现场实验的结果,并单独使用ARD得出了类似的结论。
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Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data.
Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form "how many of your links have trait k ?" Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.
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来源期刊
CiteScore
18.60
自引率
2.80%
发文量
122
期刊介绍: The American Economic Review (AER) stands as a prestigious general-interest economics journal. Founded in 1911, it holds the distinction of being one of the nation's oldest and most esteemed scholarly journals in economics. With a commitment to academic excellence, the AER releases 12 issues annually, featuring articles that span a wide spectrum of economic topics.
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