蝌蚪网 Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Temporal Aggregation for the Synthetic Control Method
蝌蚪网 Papers and Proceedings
vol. 114,
May 2024
(pp. 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.Citation
Sun, Liyang, Eli Ben-Michael, and Avi Feller. 2024. "Temporal Aggregation for the Synthetic Control Method." 蝌蚪网 Papers and Proceedings 114: 614–17. DOI: 10.1257/pandp.20241050Additional Materials
JEL Classification
- C13 Estimation: General
- C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- C51 Model Construction and Estimation