Pass‐Through and the Prediction of Merger Price Effects
通过蒙特卡洛实验研究传递率如何改进并购价格预测，比较一阶近似法和基于传递率选择函数形式的方法，发现两者均优于标准模拟，但后者对测量误差更稳健。
We use Monte Carlo experiments to study how pass‐through can improve merger price predictions, focusing on the first order approximation (FOA) proposed in Jaffe and Weyl [ ]. FOA addresses the functional form misspecification that can exist in standard merger simulations. We find that the predictions of FOA are tightly distributed around the true price effects if pass‐through is precise, but that measurement error in pass‐through diminishes accuracy. As a comparison to FOA, we also study a methodology that uses pass‐through to select among functional forms for use in simulation. This alternative also increases accuracy relative to standard merger simulation and proves more robust to measurement error.