Research attempting to predict repression, including the policing of protest, has tended to rely on pooled time series data, which statistically produces coefficients that estimate the average relationship between each variable and the outcome across the entire pooled time period. When relationships are very stable, this statistical assumption, referred to as temporal homogeneity, is unproblematic. But, when enforced without testing, it threatens to artificially “stabilize” temporally heterogenous relationships. In terms of protest policing, this has resulted in relatively ahistorical empirical explanations of protest policing. This article imports modeling techniques from work on identifying historical periods to show how temporal moving regressions can be built to recognize and model temporal heterogeneity in the factors influencing protest policing. We present three important uses for these models: testing exhaustively for temporal heterogeneity in apparently stable findings; testing for temporal heterogeneity that may reconcile otherwise contradictory findings; and inductively combining orthogonal research lines. We demonstrate the utility of each in examinations of protest policing. More generally, we show the potential of temporal moving regressions for uncovering new insights and bringing greater historical sensitivity to research on protest and beyond.

试图预测镇压的研究，包括对抗议的监管，往往依赖于汇总的时间序列数据，该数据从统计上产生系数，用于估计整个汇总时间段内每个变量与结果之间的平均关系。当关系非常稳定时，这种称为时间同质性的统计假设是没有问题的。但是，如果在未经测试的情况下强制执行，它可能会人为地“稳定”时间上的异质关系。就抗议警务而言，这导致了对抗议警务的相对非历史的实证解释。本文从识别历史时期的工作中引入建模技术，以展示如何构建时间移动回归来识别和建模影响抗议警察的因素中的时间异质性。我们介绍了这些模型的三个重要用途：在明显稳定的结果中详尽地测试时间异质性；测试时间异质性可能会调和其他相互矛盾的发现；并归纳结合正交研究路线。我们展示了每个人在抗议警务检查中的效用。更一般地说，我们展示了时间移动回归的潜力，用于揭示新见解并为抗议及其他研究带来更大的历史敏感性。彻底检验明显稳定的结果的时间异质性；测试时间异质性可能会调和其他相互矛盾的发现；并归纳结合正交研究路线。我们展示了每个人在抗议警务检查中的效用。更一般地说，我们展示了时间移动回归的潜力，用于揭示新见解并为抗议及其他研究带来更大的历史敏感性。彻底检验明显稳定的结果的时间异质性；测试时间异质性可能会调和其他相互矛盾的发现；并归纳结合正交研究路线。我们展示了每个人在抗议警务检查中的效用。更一般地说，我们展示了时间移动回归的潜力，用于揭示新见解并为抗议及其他研究带来更大的历史敏感性。