Prediction Intervals for Synthetic Control Methods
Matias Cattaneo,
Yingjie Feng and
Rocio Titiunik
Papers from arXiv.org
Abstract:
Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop conditional prediction intervals in the SC framework, and provide conditions under which these intervals offer finite-sample probability guarantees. Our method allows for covariate adjustment and non-stationary data. The 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 simulation-based approach along with finite-sample-based probability bound arguments, naturally leading to principled sensitivity analysis methods. We illustrate the numerical performance of our methods using empirical applications and a small simulation study. \texttt{Python}, \texttt{R} and \texttt{Stata} software packages implementing our methodology are available.
Date: 2019-12, Revised 2021-09
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Citations: View citations in EconPapers (26)
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http://arxiv.org/pdf/1912.07120 Latest version (application/pdf)
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Journal Article: Prediction Intervals for Synthetic Control Methods (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1912.07120
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