Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German reunification. We argue that selecting controls using a time series strategy is preferable to existing data‐driven regression methods.

中文翻译：

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协整和控制：使用时间序列数据评估事件的影响

对照组可以提供反事实证据，以评估事件或政策变化对目标变量的影响。我们认为，通过对目标与控件的加权平均值进行直接比较，拟合多元时间序列模型可提供潜在的收益。更重要的是，它突出显示了诸如差异之差和综合控制之类的方法所基于的假设，并提出了检验这些假设的方法。使用包括1989年加利福尼亚州吸烟法和德国统一在内的文献中的示例分析了简单透明的时间序列模型的收益。我们认为，使用时间序列策略选择控件比现有的数据驱动回归方法更可取。