| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28885 |
| 来源ID | Working Paper 28885 |
| The Augmented Synthetic Control Method | |
| Eli Ben-Michael; Avi Feller; Jesse Rothstein | |
| 发表日期 | 2021-06-07 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pre-treatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pre-treatment fit is infeasible. Analogous to bias correction for inexact matching, Augmented SCM uses an outcome model to estimate the bias due to imperfect pre-treatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pre-treatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Fiscal Policy ; Subnational Fiscal Issues |
| URL | https://www.nber.org/papers/w28885 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586559 |
| 推荐引用方式 GB/T 7714 | Eli Ben-Michael,Avi Feller,Jesse Rothstein. The Augmented Synthetic Control Method. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28885.pdf（5833KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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