A Permutation-Based Changepoint Technique for Monitoring Effect Sizes
A Permutation-Based Changepoint Technique for Monitoring Effect Sizes
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DOI：
10.1017/pan.2020.44
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发表时间：
2021-01
影响因子：
5.4
通讯作者：
D. Kent;James D. Wilson;Skyler J. Cranmer
中科院分区：
文献类型：
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作者：
D. Kent;James D. Wilson;Skyler J. Cranmer
摘要在社会科学领域，学者们经常在相当长的一段时间内汇集效应，如果底层数据生成过程是动态的，这种做法会产生错误的推论。为了帮助研究人员更好地进行时变过程的原则性分析，我们开发了一个两阶段的程序，基于置换测试和统计过程监控技术。给定时间序列的横截面数据，我们打破了时间的作用，通过置换推理，并产生一个零分布，反映了一个时不变的数据生成过程。然后，零分布用作稳定的参考点，使得能够检测效果变化点。在蒙特卡罗模拟中，我们的随机化技术优于变点分析的替代方案。我们的方法的一个特别的好处是，通过在与实际估计相互作用之前建立时不变效应的界限，它能够区分随机波动和真正的变化。我们证明了该方法的实用性，将其应用到一个流行的研究联盟之间的关系和军事化的国家间争端的启动。这个例子说明了该技术如何帮助研究人员推断动态关系中发生变化的地方，并提出有关这些变化的重要问题。
Abstract Across the social sciences, scholars regularly pool effects over substantial periods of time, a practice that produces faulty inferences if the underlying data generating process is dynamic. To help researchers better perform principled analyses of time-varying processes, we develop a two-stage procedure based upon techniques for permutation testing and statistical process monitoring. Given time series cross-sectional data, we break the role of time through permutation inference and produce a null distribution that reflects a time-invariant data generating process. The null distribution then serves as a stable reference point, enabling the detection of effect changepoints. In Monte Carlo simulations, our randomization technique outperforms alternatives for changepoint analysis. A particular benefit of our method is that, by establishing the bounds for time-invariant effects before interacting with actual estimates, it is able to differentiate stochastic fluctuations from genuine changes. We demonstrate the method’s utility by applying it to a popular study on the relationship between alliances and the initiation of militarized interstate disputes. The example illustrates how the technique can help researchers make inferences about where changes occur in dynamic relationships and ask important questions about such changes.