Abstract
We investigate the properties of a systematic bias that arises in the synthetic control estimator in panel data settings with finite pre-treatment periods, offering intuition and guidance to practitioners. The bias comes from matching to idiosyncratic error terms (noise) in the treated unit and the donor units’ pre-treatment outcome values. This in turn leads to a biased counterfactual for the post-treatment periods. We use Monte Carlo simulations to evaluate the determinants of the bias in terms of error term variance, sample characteristics and DGP complexity, providing guidance as to which situations are likely to yield more bias. We also offer a procedure to reduce the bias using a direct computational bias-correction procedure based on re-sampling from a pilot model that can reduce the bias in empirically feasible implementations. As a final potential solution, we compare the performance of our corrections to that of an Interactive Fixed Effects model. An empirical application focused on trade liberalization indicates that the magnitude of the bias may be economically meaningful in a real world setting.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/jem-2021-0019).
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Articles in the same Issue
- Frontmatter
- Research Articles
- Identifying Common and Idiosyncratic Explosive Behaviors in the Large Dimensional Factor Model with an Application to U.S. State-Level House Prices
- Estimation in the Presence of Heteroskedasticity of Unknown Form: A Lasso-based Approach
- Nonparametric Instrumental Regression with Two-Way Fixed Effects
- Matching on Noise: Finite Sample Bias in the Synthetic Control Estimator
- Does Health Behavior Change After Diagnosis? Evidence From Fuzzy Regression Discontinuity
- Teaching Corner
- Introduction to Latent Variable Estimation for Undergraduate Econometrics: An Application with Disney Theme Park Ride Wait Times
- Practitioner’s Corner
- Neglected Heterogeneity, Simpson’s Paradox, and the Anatomy of Least Squares
- Review
- Estimation of Causal Effects with a Binary Treatment Variable: A Unified M-Estimation Framework
Articles in the same Issue
- Frontmatter
- Research Articles
- Identifying Common and Idiosyncratic Explosive Behaviors in the Large Dimensional Factor Model with an Application to U.S. State-Level House Prices
- Estimation in the Presence of Heteroskedasticity of Unknown Form: A Lasso-based Approach
- Nonparametric Instrumental Regression with Two-Way Fixed Effects
- Matching on Noise: Finite Sample Bias in the Synthetic Control Estimator
- Does Health Behavior Change After Diagnosis? Evidence From Fuzzy Regression Discontinuity
- Teaching Corner
- Introduction to Latent Variable Estimation for Undergraduate Econometrics: An Application with Disney Theme Park Ride Wait Times
- Practitioner’s Corner
- Neglected Heterogeneity, Simpson’s Paradox, and the Anatomy of Least Squares
- Review
- Estimation of Causal Effects with a Binary Treatment Variable: A Unified M-Estimation Framework