The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data over time can mitigate these problems but can also destroy important signal. In this paper, we bound the bias for SCM with disaggregated and aggregated outcomes and give conditions under which aggregating tightens the bounds. We then propose finding weights that balance both disaggregated and aggregated series.

翻译：合成控制法（SCM）是一种利用面板数据估计单一单元处理效应的流行方法。高频数据（如月度数据相较于年度数据）带来两个挑战：（1）实现出色的预处理拟合通常更为困难；（2）过度拟合噪声的可能性更大。对数据进行时间聚合可以缓解这些问题，但也可能破坏重要信号。本文中，我们界定了基于非聚合与聚合结果使用SCM时的偏差范围，并给出了聚合能够缩小偏差范围的条件。随后，我们提出一种寻找既能平衡非聚合序列又能平衡聚合序列的权重的方法。