Standard Synthetic Control Methods: The Case of Using All Preintervention Outcomes Together With Covariates

Ashok Kaul, Stefan Klößner, Gregor Pfeifer and Manuel Schieler

Journal of Business & Economic Statistics, 2022, vol. 40, issue 3, 1362-1376

Abstract: It is becoming increasingly popular in applications of standard synthetic control methods to include the entire pretreatment path of the outcome variable as economic predictors. We demonstrate both theoretically and empirically that using all outcome lags as separate predictors renders all other covariates irrelevant in such settings. This finding holds irrespective of how important these covariates are for accurately predicting posttreatment values of the outcome, threatening the estimator’s unbiasedness. We show that estimation results and corresponding policy conclusions can change considerably when the usage of outcome lags as predictors is restricted, resulting in other covariates obtaining positive weights. Monte Carlo studies examine potential bias.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2021.1930012 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:40:y:2022:i:3:p:1362-1376

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2021.1930012

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().