Sun, Liyang;
Ben-Michael, Eli;
Feller, Avi;
(2024)
Temporal Aggregation for the Synthetic Control Method.
In: Johnson, William R, (ed.)
Proceedings of the One Hundred Thirty-Sixth Annual Meeting of the American Economic Association.
(pp. pp. 614-617).
American Economic Association (AEA): San Antonio, TX, USA.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Temporal Aggregation for the Synthetic Control Method |
| Event: | One Hundred Thirty-Sixth Annual Meeting of the American Economic Association |
| Location: | San Antonio, US |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1257/pandp.20241050 |
| Publisher version: | https://doi.org/10.1257/pandp.20241050 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10186659 |
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