Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods.

交叉滞后面板模型（CLPM）很常见，但它们的应用通常侧重于时间近端观察结果中的“短期”效应。这解决了有关动态系统如何立即响应干预措施的问题，但未能显示出系统如何在更长的时间内发展。我们探索动态系统中的三种“长期”效应，这些效应扩展了有关“脉冲响应”的最新研究成果，反映了一次性干预的潜在长期效应。超越这些，我们首先通过测试“永久效应”来处理系统（不稳定性）的评估，这很重要，因为在不稳定的系统中，即使是一次干预也可能会产生持久的影响。其次，我们探索经典的计量经济学长期效应，这些效应表明动态系统如何响应随时间而持续的干预措施。第三，我们使用“累积响应”来建模系统如何随着时间的推移对重复的干预做出响应。我们在模拟数据集中说明了每种长期效应的测试，并在线提供了所有资料，包括用户友好的R代码，该代码可自动估算，测试，报告和绘制所有效应（请参阅https://doi.org/10.26188/13506861 ）。我们通过强调将特定的纵向假设与定量方法保持一致的价值来得出结论。