Challenges in Merger Simulation Analysis
基于随机系数Logit需求模型和静态伯特兰供给假设，展示不同优化算法和初始值组合导致的需求估计差异，会显著影响合并后市场结果的预测，如行业利润、消费者福利和价格变化。
In this paper, we share our experience with merger simulations using a Random Coefficient Logit model on the demand side and assuming a static Bertrand game on the supply side. Drawing largely from our work in Knittel and Metaxoglou (2008), we show that different demand estimates obtained from different combinations of optimization algorithms and starting values lead to substantial differences in post-merger market outcomes using metrics such as industry profits, and change in consumer welfare and prices.