UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Temporal Aggregation for the Synthetic Control Method

Sun, Liyang; Ben-Michael, Eli; Feller, Avi; (2024) Temporal Aggregation for the Synthetic Control Method. In: American Economic Association. Papers and Proceedings. (In press).

[thumbnail of multi_outcomes_aeapp (8).pdf] Text
multi_outcomes_aeapp (8).pdf - Other
Access restricted to UCL open access staff until 8 August 2024.

Download (306kB)

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.

Type: Proceedings paper
Title: Temporal Aggregation for the Synthetic Control Method
Event: ASSA 2024 Annual Meeting
Location: San Antonio, US
Publisher version: https://www.aeaweb.org/journals/pandp/about-pandp
Language: English
Additional information: This version is the author accepted manuscript. 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
Downloads since deposit
1Download
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item