Using Raw VAR Regression Coefficients to Build Networks can be Misleading
Using Raw VAR Regression Coefficients to Build Networks can be Misleading
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DOI：
10.1080/00273171.2016.1150151
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发表时间：
2016-01-01
影响因子：
3.8
通讯作者：
Ceulemans, Eva
中科院分区：
文献类型：
--
作者：
Bulteel, Kirsten;Tuerlinckx, Francis;Ceulemans, Eva
行为科学中的许多问题都集中在一些变量随时间变化的因果相互作用上。为了揭示变量之间的动态关系，可以通过拟合滞后一向量自回归或VAR(1)模型并将得到的回归系数可视化为加权有向网络的边来检查它们随时间的(自回归或交叉)回归效应。通常会绘制原始的VAR(1)回归系数，但我们认为，由于两个问题，这可能会产生误导性的网络图形和特征。首先，原始回归系数对变量之间的规模和方差差异很敏感，因此可能缺乏可比性，例如，如果想要计算中心性衡量标准，就需要可比性。其次，它们只代表变量的独特直接影响，当变量相关性很强时，可能会给出一幅扭曲的图景。为了解决这些问题，我们建议使用其他基于VAR(1)的度量作为边缘。具体地说，为了解决可比性问题，可以显示标准化的VAR(1)回归系数。此外，可以计算相对重要性度量，以包括对网络的直接影响以及共享和间接影响。
Many questions in the behavioral sciences focus on the causal interplay of a number of variables across time. To reveal the dynamic relations between the variables, their (auto- or cross-) regressive effects across time may be inspected by fitting a lag-one vector autoregressive, or VAR(1), model and visualizing the resulting regression coefficients as the edges of a weighted directed network. Usually, the raw VAR(1) regression coefficients are drawn, but we argue that this may yield misleading network figures and characteristics because of two problems. First, the raw regression coefficients are sensitive to scale and variance differences among the variables and therefore may lack comparability, which is needed if one wants to calculate, for example, centrality measures. Second, they only represent the unique direct effects of the variables, which may give a distorted picture when variables correlate strongly. To deal with these problems, we propose to use other VAR(1)-based measures as edges. Specifically, to solve the comparability issue, the standardized VAR(1) regression coefficients can be displayed. Furthermore, relative importance metrics can be computed to include direct as well as shared and indirect effects into the network.