arXiv 5 Jun 2023 · Econometrics
arXiv:2306.02584 · PDF · DOI · OpenAlex · Extracted main text
Estimating weights in the synthetic control method, typically resulting in sparse weights where only a few control units have non-zero weights, involves an optimization procedure that simultaneously selects and aligns control units to closely match the treated unit. However, this simultaneous selection and alignment of control units may lead to a loss of efficiency. Another concern arising from the aforementioned procedure is its susceptibility to under-fitting due to imperfect pre-treatment fit. It is not uncommon for the linear combination, using nonnegative weights, of pre-treatment period outcomes for the control units to inadequately approximate the pre-treatment outcomes for the treated unit. To address both of these issues, this paper proposes a simple and effective method called Synthetic Regressing Control (SRC). The SRC method begins by performing the univariate linear regression to appropriately align the pre-treatment periods of the control units with the treated unit. Subsequently, a SRC estimator is obtained by synthesizing (taking a weighted average) the fitted controls. To determine the weights in the synthesis procedure, we propose an approach that utilizes a criterion of unbiased risk estimator. Theoretically, we show that the synthesis way is asymptotically optimal in the sense of achieving the lowest possible squared error. Extensive numerical experiments highlight the advantages of the SRC method.
appendix boundary found by appendix_command · 77% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Abadie, A (2021) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects | 1.000 | 5 | 3 | 100% |
| 2 | Abadie, A. and Diamond, A. and Hainmueller, J (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of Cali- fornia's Tobacco Control Program | 0.961 | 9 | 6 | 89% |
| 3 | Abadie, A. and Gardeazabal, J (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.928 | 4 | 3 | 100% |
| 4 | Abadie, A. and L'Hour, J (2021) A Penalized Synthetic Control Estimator for Disaggregated Data | 0.928 | 4 | 3 | 100% |
| 5 | Kellogg, M. and Mogstad, M. and Guillaume A. Pouliot, G.A. and Torgo… (2021) Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation | 0.928 | 4 | 3 | 100% |
| 6 | Xu, Y (2017) Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models | 0.928 | 4 | 3 | 100% |
| 7 | Ferman, B. and Pinto, C (2021) Synthetic Controls with Imperfect Pretreatment Fit | 0.843 | 3 | 3 | 100% |
| 8 | Hsiao, C. and Ching, S. and Wan, K.S (2012) A Panel Data Approach for Program Evaluation: Measuring the Benefits of Political and Economic Integration of Hong Kong With Mai… | 0.737 | 3 | 2 | 100% |
| 9 | Zhu, L.P. and Li, L. and Li, R. and Zhu, L.X (2011) Model-free Feature Screening for Ultrahigh-dimensional Data | 0.644 | 4 | 1 | 100% |
| 10 | Ben-Michael, E. and Feller, A. and Othstein, J (2021) The Augmented Synthetic Control Method | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.