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Temporal Aggregation for the Synthetic Control Method

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  • Liyang Sun
  • Eli Ben-Michael
  • Avi Feller

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): (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.

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  • Liyang Sun & Eli Ben-Michael & Avi Feller, 2024. "Temporal Aggregation for the Synthetic Control Method," Papers 2401.12084, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2401.12084
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    References listed on IDEAS

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    1. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
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