Guillaume Allaire Pouliot, Zhen Xie
arXiv 6 Jul 2022 · Econometrics
arXiv:2207.02943 · PDF · DOI · OpenAlex · Extracted main text
We provide an analytical characterization of the model flexibility of the synthetic control method (SCM) in the familiar form of degrees of freedom. We obtain estimable information criteria. These may be used to circumvent cross-validation when selecting either the weighting matrix in the SCM with covariates, or the tuning parameter in model averaging or penalized variants of SCM. We assess the impact of car license rationing in Tianjin and make a novel use of SCM; while a natural match is available, it and other donors are noisy, inviting the use of SCM to average over approximately matching donors. The very large number of candidate donors calls for model averaging or penalized variants of SCM and, with short pre-treatment series, model selection per information criteria outperforms that per cross-validation.
appendix boundary found by appendix_command · 50% 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, Alberto and Diamond, Alexis and Hainmueller, Jens (2010) Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program | 1.000 | 8 | 4 | 100% |
| 2 | Doudchenko, Nikolay and Imbens, Guido W (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis | 1.000 | 5 | 3 | 100% |
| 3 | Abadie, Alberto and L'Hour, Jérémy (2021) A penalized synthetic control estimator for disaggregated data | 0.928 | 10 | 5 | 80% |
| 4 | Meyer, Mary and Woodroofe, Michael (2000) On the degrees of freedom in shape-restricted regression | 0.928 | 5 | 3 | 80% |
| 5 | Zou, Hui and Hastie, Trevor and Tibshirani, Robert (2007) On the “degrees of freedom” of the lasso | 0.843 | 10 | 3 | 60% |
| 6 | Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens (2015) Comparative politics and the synthetic control method | 0.843 | 3 | 3 | 100% |
| 7 | Ferman, Bruno and Pinto, Cristine (2021) Synthetic controls with imperfect pretreatment fit | 0.843 | 3 | 3 | 100% |
| 8 | Kellogg, Maxwell and Mogstad, Magne and Pouliot, Guillaume A and Tor… (2021) Combining matching and synthetic control to trade off biases from extrapolation and interpolation self | 0.811 | 4 | 2 | 100% |
| 9 | Tibshirani, Ryan and Taylor, Jonathan (2012) Degrees of freedom in lasso problems | 0.754 | 7 | 3 | 43% |
| 10 | Chen, Xi and Lin, Qihang and Sen, Bodhisattva (2020) On degrees of freedom of projection estimators with applications to multivariate nonparametric regression | 0.737 | 5 | 3 | 40% |
Showing the top 10 of 60 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Predictor Selection for Synthetic Controls | 0.405 | 1 | 1 |
| 2 | Synthetic Regressing Control | 0.405 | 1 | 1 |