We have had very helpful conversations with Lanier Benkard, Allan Collard-Wexler, Uli Doraszelski, Ariel Pakes, Carlos Santos, and Ben Van Roy, as well as seminar participants at Columbia Business School, IIOC, Informs, MSOM Conference, the Econometric Society Summer Meeting, and the NYU-Kansas City Fed Workshop on Computational Economics. We thank the editor, Phil Haile, and two anonymous referees for suggestions that greatly improved the article. The research of Farias was supported, in part, by the Solomon Buchsbaum Research Fund.
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An approximate dynamic programming approach to solving dynamic oligopoly models
Article first published online: 19 JUN 2012
DOI: 10.1111/j.1756-2171.2012.00165.x
© 2012, RAND.
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How to Cite
Farias, V., Saure, D. and Weintraub, G. Y. (2012), An approximate dynamic programming approach to solving dynamic oligopoly models. The RAND Journal of Economics, 43: 253–282. doi: 10.1111/j.1756-2171.2012.00165.x
Publication History
- Issue published online: 19 JUN 2012
- Article first published online: 19 JUN 2012
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.