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Prediction Intervals for Synthetic Control Methods

Author

Listed:
  • Matias D. Cattaneo
  • Yingjie Feng
  • Rocio Titiunik
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.

Suggested Citation

  • Matias D. Cattaneo & Yingjie Feng & Rocio Titiunik, 2019. "Prediction Intervals for Synthetic Control Methods," Papers 1912.07120, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:1912.07120
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    1. Horiuchi, Yusaku & Mayerson, Asher, 2015. "The Opportunity Cost of Conflict: Statistically Comparing Israel and Synthetic Israel," Political Science Research and Methods, Cambridge University Press, vol. 3(3), pages 609-618, September.
    2. Kathleen T. Li, 2020. "Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2068-2083, December.
    3. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    4. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    5. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," Papers 1212.6906, arXiv.org, revised Jan 2018.
    6. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    7. Doukhan, Paul & Louhichi, Sana, 1999. "A new weak dependence condition and applications to moment inequalities," Stochastic Processes and their Applications, Elsevier, vol. 84(2), pages 313-342, December.
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    Cited by:

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    10. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    11. Priscila Espinosa & Daniel Aparicio-Pérez & Emili Tortosa-Ausina, 2023. "On the Impact of Next Generation EU Funds: A Regional Synthetic Control Method Approach," Working Papers 2023/07, Economics Department, Universitat Jaume I, Castellón (Spain).
    12. Emery Thomas J. & Kovac Mitja & Spruk Rok, 2023. "Estimating the Effects of Political Instability in Nascent Democracies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(6), pages 599-642, December.
    13. Guido W. Imbens & Davide Viviano, 2023. "Identification and Inference for Synthetic Controls with Confounding," Papers 2312.00955, arXiv.org.
    14. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    15. Guillaume Allaire Pouliot & Zhen Xie, 2022. "Degrees of Freedom and Information Criteria for the Synthetic Control Method," Papers 2207.02943, arXiv.org.
    16. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    17. Matias D. Cattaneo & Yingjie Feng & Filippo Palomba & Rocio Titiunik, 2022. "Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption," Papers 2210.05026, arXiv.org, revised Oct 2024.
    18. Ursula Muench & Armin Nassehi & Joe Kaeser & Knut Bergmann & Matthias Diermeier & Florian Dorn & David Gstrein & Florian Neumeier & Manuel Funke & Moritz Schularick & Christoph Trebesch & Kerim Peren , 2024. "Wohlstand in Gefahr? Ursachen und Folgen von Populismus," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 77(03), pages 03-32, March.
    19. David M. Ritzwoller & Joseph P. Romano & Azeem M. Shaikh, 2024. "Randomization Inference: Theory and Applications," Papers 2406.09521, arXiv.org.
    20. Matias D. Cattaneo & Yingjie Feng & Filippo Palomba & Rocio Titiunik, 2022. "scpi: Uncertainty Quantification for Synthetic Control Methods," Papers 2202.05984, arXiv.org, revised Oct 2022.
    21. Wei Tian & Seojeong Lee & Valentyn Panchenko, 2023. "Synthetic Controls with Multiple Outcomes," Papers 2304.02272, arXiv.org, revised Jul 2024.

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