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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): (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.

Suggested Citation

  • Liyang Sun & Eli Ben-Michael & Avi Feller, 2024. "Temporal Aggregation for the Synthetic Control Method," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 614-617, May.
    • Handle: RePEc:aea:apandp:v:114:y:2024:p:614-17
    DOI: 10.1257/pandp.20241050
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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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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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