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：合成控制方法（SCM）是一种利用面板数据估算单个受干预单元处理效应的流行方法。高频数据（如月度数据相对于年度数据）会带来两个挑战：（1）实现优异的预处理期拟合通常更为困难；（2）对噪声的过拟合可能性更高。对数据进行时间聚合可缓解这些问题，但也可能破坏重要信号。本文界定了使用离散化与聚合化结果变量时SCM的偏差范围，并给出了聚合能够收紧偏差范围的条件。随后，我们提出了同时平衡离散化序列与聚合化序列的权重求解方法。