Synthetic Control with Time Varying Coefficients A State Space Approach with Bayesian Shrinkage

Danny Klinenberg

Journal of Business & Economic Statistics, 2023, vol. 41, issue 4, 1065-1076

Abstract: Synthetic control methods are a popular tool for measuring the effects of policy interventions on a single treated unit. In practice, researchers create a counterfactual using a linear combination of untreated units that closely mimic the treated unit. Oftentimes, creating a synthetic control is not possible due to untreated units’ dynamic characteristics such as integrated processes or a time varying relationship. These are cases in which viewing the counterfactual estimation problem as a cross-sectional one fails. In this article, I investigate a new approach to estimate the synthetic control counterfactual incorporating time varying parameters to handle such situations. This is done using a state space framework and Bayesian shrinkage. The dynamics allow for a closer pretreatment fit leading to a more accurate counterfactual estimate. Monte Carlo simulations are performed showcasing the usefulness of the proposed model in a synthetic control setting. I then compare the proposed model to existing approaches in a classic synthetic control case study.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2022.2102025 (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:41:y:2023:i:4:p:1065-1076

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

DOI: 10.1080/07350015.2022.2102025

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 ().