Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata
Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata
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DOI：
10.1177/1536867x211000030
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发表时间：
2021-03-01
期刊：
影响因子：
4.8
通讯作者：
Laub, Patrick J.
中科院分区：
文献类型：
--
作者：
Li, Jinjing;Zyphur, Michael J.;Laub, Patrick J.
社会和健康研究人员如何研究以非线性甚至混乱方式运作的复杂动态系统？常见的方法，如实验和基于方程的模型，可能不适合这项任务。为了解决现有方法的局限性，并提供表征和测试非线性动态系统因果关系的非参数工具，我们在Stata中引入了edm命令。该命令为时间序列和面板数据实现了三种关键的经验动态建模（EDM）方法：1)单纯形投影，它表征了系统的维度及其确定性功能的程度；2) s映射，用于量化系统的非线性程度；3)收敛交叉映射，为因果效应建模提供了非参数方法。我们使用芝加哥每日温度和犯罪的模拟数据来说明这些方法，显示温度对犯罪的影响，而不是相反。最后，我们讨论了EDM如何允许检查传统基于模型的方法的假设，如残差自相关检验，我们提倡EDM，因为它不假设线性、稳定性或平衡。
How can social and health researchers study complex dynamic systems that function in nonlinear and even chaotic ways? Common methods, such as experiments and equation-based models, may be ill-suited to this task. To address the limitations of existing methods and offer nonparametric tools for characterizing and testing causality in nonlinear dynamic systems, we introduce the edm command in Stata. This command implements three key empirical dynamic modeling (EDM) methods for time series and panel data: 1) simplex projection, which characterizes the dimensionality of a system and the degree to which it appears to function deterministically; 2) S-maps, which quantify the degree of nonlinearity in a system; and 3) convergent cross-mapping, which offers a nonparametric approach to modeling causal effects. We illustrate these methods using simulated data on daily Chicago temperature and crime, showing an effect of temperature on crime but not the reverse. We conclude by discussing how EDM allows checking the assumptions of traditional model-based methods, such as residual autocorrelation tests, and we advocate for EDM because it does not assume linearity, stability, or equilibrium.