An approximate dynamic programming approach to solving dynamic oligopoly models

In this paper 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 based on linear programming. We provide results that lend theoretical support to our approximation. We test our method on an important class of models based on Pakes and McGuire (1994). Our results suggest that the approach we propose significantly expands the set of dynamic oligopoly models that can be analyzed computationally.