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Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods.
1909
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Abadie, A. (2021), “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects,” Journal of Economic Literature , 59, 391–425
2021
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Abadie, A., and L’Hour, J. (2021), “A Penalized Synthetic Control Estimator for Disaggregated Data,” Journal of the American Statistical Association
2021
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2021
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Chernozhukov, V., Wüthrich, K., and Zhu, Y. (2021b), “An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls,” Journal of the American Statistical Association
Cited in the paper.
Ferman, B. (2021), “On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls,” Journal of the American Statistical Association
2021
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Ferman, B., and Pinto, C. (2021), “Synthetic Controls with Imperfect Pre-Treatment Fit,” Quantitative Economics
2021
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Kellogg, M., Mogstad, M., Pouliot, G., and Torgovitsky, A. (2021), “Combining Matching and Synthetic Controls to Trade off Biases from Extrapolation and Interpolation,” Journal of the American Statistical Association
2021
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Masini, R., and Medeiros, M. C. (2021), “Counterfactual Analysis with Artificial Controls: Inference, High Dimensions and Nonstationarity,” Journal of the American Statistical Association
2021
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2021
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Li, K. T. (2020), “Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods,” Journal of the American Statistical Association , 115, 2068–2083
2083
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