EconPapers    
Economics at your fingertips  
 

Revisiting the Synthetic Control Estimator

Bruno Ferman and Cristine Pinto ()

MPRA Paper from University Library of Munich, Germany

Abstract: The synthetic control (SC) method has been recently proposed as an alternative to estimate treatment effects in comparative case studies. The SC relies on the assumption that there is a weighted average of the control units that reconstructs the potential outcome of the treated unit in the absence of treatment. If these weights were known, then constructing the counterfactual for the treated unit using a weighted average of the control units would provide an unbiased estimator for the treatment effect, even if selection into treatment is correlated with the unobserved heterogeneity. In this paper, we revisit the SC method in a linear factor model where the SC weights are considered nuisance parameters that are estimated to construct the SC estimator. We show that, when the number of control units is fixed, the estimated SC weights will generally not converge to the weights that reconstruct the factor loadings of the treated unit, even when the number of pre-intervention periods goes to infinity. As a consequence, the SC estimator will be asymptotically biased if treatment assignment is correlated with the unobserved heterogeneity. The asymptotic bias only vanishes when the variance of the idiosyncratic error goes to zero. We suggest a slight modification in the SC method that guarantees that the SC estimator is asymptotically unbiased and has a lower asymptotic variance than the difference-in-differences (DID) estimator when the DID identification assumption is satisfied. We also propose an alternative way to estimate the SC weights that provides an asymptotically unbiased estimator under additional assumptions on the error structure. Finally, we consider the implications of our findings to the permutation test suggested in Abadie et al. (2010).

Keywords: synthetic control, difference-in-differences; linear factor model, inference, permutation test (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 C23 (search for similar items in EconPapers)
Date: 2016-09-23
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12) Track citations by RSS feed

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/73982/1/MPRA_paper_73982.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/75128/1/MPRA_paper_75128.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/77930/1/MPRA_paper_77930.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/80685/1/MPRA_paper_80685.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/81941/1/MPRA_paper_81941.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/86495/1/MPRA_paper_86495.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/95524/1/MPRA_paper_95524.pdf revised version (application/pdf)

Related works:
Working Paper: Revisiting the synthetic control estimator (2016) Downloads
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:pra:mprapa:73982

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

 
Page updated 2019-11-26
Handle: RePEc:pra:mprapa:73982