论文标题
合成控制方法的自由度和信息标准
Degrees of Freedom and Information Criteria for the Synthetic Control Method
论文作者
论文摘要
我们以熟悉的自由度形式提供了合成控制方法（SCM）模型灵活性的分析表征。我们获得了可估计的信息标准。当选择协变量中的SCM中的加权矩阵或模型平均或惩罚的SCM变体中的调谐参数时，这些可用于规避交叉验证。我们评估了天津汽车许可定量的影响，并对SCM进行了新的使用；尽管有自然的比赛，但它和其他捐助者都很嘈杂，邀请SCM在大约匹配的捐助者中平均使用。大量的候选捐助者要求SCM的模型平均或受惩罚变体，并且在较短的预处理系列中，每个信息标准的模型选择都优于每个交叉验证。
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.
