Becker, Martin and Klößner, Stefan and Pfeifer, Gregor (2017): Cross-Validating Synthetic Controls.
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Abstract
While the literature on synthetic control methods mostly abstracts from out-of-sample measures, Abadie et al. (2015) have recently introduced a cross-validation approach. This technique, however, is not well-defined since it hinges on predictor weights which are not uniquely defined. We fix this issue, proposing a new, well-defined cross-validation technique, which we apply to the original Abadie et al. (2015) data. Additionally, we discuss how this new technique can be used for comparing different specifications based on out-of-sample measures, avoiding the danger of cherry-picking.
Item Type: | MPRA Paper |
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Original Title: | Cross-Validating Synthetic Controls |
Language: | English |
Keywords: | Synthetic Control Methods; Cross-Validation; Specification Search. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection |
Item ID: | 83679 |
Depositing User: | Dr. Gregor Pfeifer |
Date Deposited: | 06 Jan 2018 12:20 |
Last Modified: | 13 Nov 2024 14:14 |
References: | Abadie, A., Diamond, A., and Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association, 105(490):493-505. Abadie, A., Diamond, A., and Hainmueller, J. (2015). Comparative Politics and the Synthetic Control Method. American Journal of Political Science, 59(2):495-510. Abadie, A. and Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. The American Economic Review, 93(1):113-132. Athey, S. and Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2):3-32. Becker, M. and Klößner, S. (2017a). Fast and Reliable Computation of Generalized Synthetic Controls. Econometrics and Statistics, pages n/a-n/a. Becker, M. and Klößner, S. (2017b). MSCMT: Multivariate Synthetic Control Method Using Time Series. R package version 1.3.0. Ferman, B., Pinto, C., and Possebom, V. (2017). Cherry picking with synthetic controls. Working Paper. Gardeazabal, J. and Vega-Bayo, A. (2017). An Empirical Comparison Between the Synthetic Control Method and Hsiao et al.’s Panel Data Approach to Program Evaluation. Journal of Applied Econometrics, 32(5):983-1002. Klößner, S., Kaul, A., Pfeifer, G., and Schieler, M. (2017). Comparative politics and the synthetic control method revisited: A note on Abadie et al. (2015). Swiss Journal of Economics and Statistics, pages n/a-n/a. Klößner, S. and Pfeifer, G. (2017). Outside the box: Using synthetic control methods as a forecasting technique. Applied Economics Letters, pages n/a-n/a. Montalvo, J. G. (2011). Voting after the bombings: A natural experiment on the effect of terrorist attacks on democratic elections. The Review of Economics and Statistics, 93(4):1146-1154. R Core Team (2017). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/83679 |