Methods to identify linear network models: a review.

Q1 Mathematics Swiss Journal of Economics and Statistics Pub Date : 2018-01-01 Epub Date: 2018-02-05 DOI:10.1186/s41937-017-0011-x
Arun Advani, Bansi Malde
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引用次数: 7

Abstract

In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the 'linear-in-means' local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.

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线性网络模型的识别方法综述。
在许多情况下,我们可能有兴趣了解代理人之间的直接联系是否会影响他们的结果,例如在教室里宣布的友谊或在农村的家庭联系。在本文中,我们回顾了研究用于分析社会效应线性模型的计量经济学方法的文献,这类模型包括“线性均值”局部平均模型、局部聚合模型和网络统计影响结果的模型。在讨论使用观测和实验/准实验数据进行识别的条件之前,我们概述了潜在的理论模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Swiss Journal of Economics and Statistics
Swiss Journal of Economics and Statistics Mathematics-Statistics and Probability
CiteScore
5.20
自引率
0.00%
发文量
18
审稿时长
15 weeks
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