Botosaru, Irene and Ferman, Bruno (2017): On the Role of Covariates in the Synthetic Control Method.
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Abstract
This note revisits the role of time-invariant observed covariates in the Synthetic Control (SC) method. We first derive conditions under which the original result of Abadie et al (2010) regarding the bias of the SC estimator remains valid when we relax the assumption of a perfect match on observed covariates and assume only a perfect match on pre-treatment outcomes. We then show that, even when the conditions for the first result are valid, a perfect match on pre-treatment outcomes does not generally imply an approximate match for all covariates. This will only be true for those that are both relevant and whose effects (over time) are not collinear with the effects of other observed and unobserved covariates. Taken together, our results show that a perfect match on covariates should not be required for the SC method, as long as there is a perfect match on a long set of pre-treatment outcomes.
Item Type: | MPRA Paper |
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Original Title: | On the Role of Covariates in the Synthetic Control Method |
Language: | English |
Keywords: | Synthetic controls, covariates, perfect match |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models |
Item ID: | 80796 |
Depositing User: | Bruno Ferman |
Date Deposited: | 16 Aug 2017 15:49 |
Last Modified: | 27 Sep 2019 13:11 |
References: | Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, “Synthetic Control Methods for Com- parative Case Studies: Estimating the Effect of California’s Tobacco Control Program,” Journal of the American Statiscal Association, 2010, 105 (490), 493–505. Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, “Comparative Politics and the Synthetic Control Method,” American Journal of Political Science, 2015, 59 (2), 495–510. Abadie, Alberto and Javier Gardeazabal, “The Economic Costs of Conflict: A Case Study of the Basque Country,” American Economic Review, 2003, 93 (1), 113–132. Athey, Susan and Guido W. Imbens, “The State of Applied Econometrics: Causality and Policy Evaluation,” Journal of Economic Perspectives, May 2017, 31 (2), 3–32. Ferman, Bruno and Cristine Pinto, “Revisiting the synthetic control estimator,” 2016. Ferman, Bruno, Cristine Pinto, and Vitor Possebom, “Cherry Picking with Synthetic Controls,” 2016. Working Paper. Kaul, Ashok, Stefan Klobner, Gregor Pfeifer, and Manuel Schieler, “Synthetic Control Methods: Never Use All Pre-Intervention Outcomes as Economic Predictores,” 2015. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/80796 |
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