Temporal Aggregation for the Synthetic Control Method

Liyang Sun, Eli Ben-Michael and Avi Feller

AEA Papers and Proceedings, 2024, vol. 114, 614-17

Abstract: 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): (i) achieving excellent pretreatment fit is typically more challenging, and (ii) 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.

JEL-codes: C13 C21 C51 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Temporal Aggregation for the Synthetic Control Method (2024)
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DOI: 10.1257/pandp.20241050

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