An approximate dynamic programming approach to solving dynamic oligopoly models
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 bes...
| Main Authors: | , , |
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| Andre forfattere: | |
| Format: | Article |
| Sprog: | en_US |
| Udgivet: |
Wiley Blackwell
2012
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| Online adgang: | http://hdl.handle.net/1721.1/74676 https://orcid.org/0000-0002-5856-9246 |
| Summary: | 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. |
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