  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.
