Revisiting the Synthetic Control Estimator

Ferman, Bruno and Pinto, Cristine (2016): Revisiting the Synthetic Control Estimator.

Abstract

We analyze the conditions under which the Synthetic Control (SC) estimator is unbiased. We show that the SC estimator is generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre-treatment periods goes to infinity, and in settings where one should expect an almost perfect pre-treatment fit. While our results suggest that researchers should be more careful in interpreting the identification assumptions required for the SC method, we show that, with a slight modification, the SC method can substantially improve in terms of bias and variance relative to standard methods.

• Item Type: MPRA Paper
• Language: English
• Keywords: synthetic control, difference-in-differences; linear factor model
• 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

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