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.

社会和卫生研究人员如何研究以非线性甚至混沌方式起作用的复杂动力系统？诸如实验和基于方程式的模型之类的常用方法可能不适合此任务。为了解决现有方法的局限性并提供用于表征和测试非线性动态系统中因果关系的非参数工具，我们引入了edm在Stata中的命令。该命令实现了用于时间序列和面板数据的三种关键的经验动态建模（EDM）方法：1）单形投影，它表征系统的维数和确定性功能的程度；2）S图，量化系统中的非线性程度；3）收敛交叉映射，它提供了一种非因果关系模型来建模因果效应。我们使用每日芝加哥温度和犯罪的模拟数据说明了这些方法，显示了温度对犯罪的影响，但并非相反。我们通过讨论EDM如何结束传统的基于模型的方法（例如残差自相关测试）的假设来进行总结，并且我们提倡EDM，因为它不假设线性，稳定性或均衡性。