Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation
提出一种匹配与合成控制（MASC）估计量，通过滚动原点交叉验证训练来权衡内插和外推偏差，并在西班牙冲突经济成本研究中应用。
The synthetic control (SC) method is widely used in comparative case studies to adjust for differences in pre-treatment characteristics. SC limits extrapolation bias at the potential expense of interpolation bias, whereas traditional matching estimators have the opposite properties. This complementarity motives us to propose a matching and synthetic control (or MASC) estimator as a model averaging estimator that combines the standard SC and matching estimators. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve trade-offs between interpolation and extrapolation bias. We use a series of empirically-based placebo and Monte Carlo simulations to shed light on when the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we apply these estimators to examine the economic costs of conflicts in the context of Spain.