An approximate dynamic programming approach to solving dynamic oligopoly models
提出一种基于近似动态规划的新方法，用于求解大规模动态寡头垄断模型中的马尔可夫完美均衡，克服了维度诅咒，显著扩展了可计算分析的模型范围。
In this article, we introduce a new method to approximate Markov perfect equilibrium in large‐scale Ericson and Pakes (1995)‐style dynamic oligopoly models that are not amenable to exact solution due to the curse of dimensionality. The method is based on an algorithm that iterates an approximate best response operator using an approximate dynamic programming approach. The method, based on mathematical programming, approximates the value function with a linear combination of basis functions. We provide results that lend theoretical support to our approach. We introduce a rich yet tractable set of basis functions, and test our method on important classes of models. Our results suggest that the approach we propose significantly expands the set of dynamic oligopoly models that can be analyzed computationally.