A General Synthetic Control Framework of Estimation and Inference

估计和推理的通用综合控制框架

• 批准号: 1756692
• 负责人: Alberto Abadie
• 金额: $ 25.88万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1756692&HistoricalAwards=false
• 关键词: General; Synthetic; Control; Framework; Estimation

Over the past decade, synthetic control estimators have found wide applicability in empirical research in economics and the social sciences as tools to evaluate the effects of public policies and other interventions or events of interests. For each unit (e.g., city, state, or region) affected by the intervention of interest, a synthetic control is a combination on unaffected units chosen to resemble the characteristics of the treated units before the intervention. Synthetic controls are used to estimate the outcomes that affected units would have attained in the absence of the intervention of interest, providing a benchmark against which researchers can evaluate policy impacts. The synthetic control method was originally developed for cases with one or a small number of aggregate units affected by the policy of interest. However, the applied literature has surpassed the methodological contributions in this area. In particular, many current applications focus on settings with a large number of affected units. As a result, additional methodological research is needed to guide the empirical implementation of the method. This research will provide empirical researchers with a robust and general framework for estimation and inference with synthetic controls as well as freely available software with which to implement it.When disaggregated data are available, constructing separate synthetic controls for each treated unit helps avoid interpolation biases. However, the problem of finding a synthetic control that best reproduces the characteristics of a treated unit may not have a unique solution, and multiplicity of solutions is a particularly daunting challenge in settings with many treated units. This research will provide a generalized synthetic control framework for estimation and inference. The framework builds on synthetic controls and introduces a penalization parameter that trades off pairwise matching discrepancies with respect to the characteristics of each unit in the synthetic control against matching discrepancies with respect to the characteristics of the synthetic control unit as a whole. It can be shown that, as long as the penalization parameter is positive, the generalized synthetic control estimator is unique and sparse. The investigators propose data driven choices for the penalization parameter, as well as inferential methods for synthetic controls in settings with many treated units.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的十年中，综合控制估计在经济学和社会科学的实证研究中发现了广泛的适用性，作为评估公共政策和其他干预措施或感兴趣的事件的影响的工具。对于每个单元（例如，城市、州或地区），综合控制是对未受影响的单元的组合，所述未受影响的单元被选择为类似于干预之前的被处理单元的特征。综合控制被用来估计在没有利益干预的情况下受影响的单位会达到的结果，为研究人员评估政策影响提供了一个基准。综合控制方法最初是为一个或少数受利益策略影响的聚合单元的情况而开发的。然而，应用文献已经超过了在这一领域的方法的贡献。特别地，许多当前应用关注具有大量受影响单元的设置。因此，需要更多的方法研究来指导该方法的实证实施。本研究将为实证研究人员提供一个强大的和一般的框架，估计和推理与合成控制，以及免费提供的软件来实现它。当分类数据可用时，构建单独的合成控制，为每个处理单元有助于避免插值偏差。然而，找到最佳再现经处理单元的特征的合成对照的问题可能没有唯一的解决方案，并且在具有许多经处理单元的设置中，解决方案的多样性是特别艰巨的挑战。这项研究将提供一个广义的综合控制框架的估计和推理。该框架建立在综合控制的基础上，并引入了一个惩罚参数，该惩罚参数权衡了相对于综合控制中每个单元的特性的成对匹配差异与相对于作为整体的综合控制单元的特性的匹配差异。结果表明，只要惩罚参数为正，广义综合控制估计器是唯一的和稀疏的。研究人员提出了惩罚参数的数据驱动的选择，以及在许多处理单元的设置中进行综合控制的推理方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Econometric Methods for Program Evaluation

• DOI: 10.1146/annurev-economics-080217-053402
• 发表时间: 2018-01-01
• 期刊: ANNUAL REVIEW OF ECONOMICS, VOL 10
• 作者: Abadie, Alberto;Cattaneo, Matias D.
• 通讯作者: Cattaneo, Matias D.

Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects

• DOI: 10.1257/jel.20191450
• 发表时间: 2021-06-01
• 期刊: JOURNAL OF ECONOMIC LITERATURE
• 影响因子: 12.6
• 作者: Abadie, Alberto

A Penalized Synthetic Control Estimator for Disaggregated Data

• DOI: 10.1080/01621459.2021.1971535
• 发表时间: 2021-11-03
• 期刊: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• 影响因子: 3.7
• 作者: Abadie, Alberto;L'Hour, Jeremy
• 通讯作者: L'Hour, Jeremy