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Prediction Intervals for Synthetic Control Methods

Matias Cattaneo (), Yingjie Feng and Rocio Titiunik ()

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Abstract: Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop prediction intervals in the canonical SC framework, and provide conditions under which these intervals offer finite-sample probability guarantees. Our construction begins by noting that the statistical uncertainty of the SC prediction is governed by two distinct sources of randomness: one coming from the construction of the (likely misspecified) SC weights in the pre-treatment period, and the other coming from the unobservable stochastic error in the post-treatment period when the treatment effect is analyzed. Accordingly, our proposed prediction intervals are constructed taking into account both sources of randomness. For implementation, we propose a multiplier bootstrap approach along with finite-sample-based probability bound arguments. We illustrate the performance of our proposed prediction intervals in the context of three empirical applications from the SC literature.

Date: 2019-12
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