A Framework for Dynamic Oligopoly in Concentrated Industries

48 Pages Posted: 1 Sep 2012

See all articles by Bar Ifrach

Bar Ifrach

Uber Technologies Inc. - Uber Freight

Gabriel Y. Weintraub

Stanford Graduate School of Business, Stanford University; Columbia University - Columbia Business School - Decision Risk and Operations

Date Written: August 30, 2012

Abstract

We consider dynamic oligopoly models in the spirit of Ericson and Pakes (1995). We introduce a new computationally tractable model for industries with a few dominant firms and many fringe firms, in which firms keep track of the detailed state of dominant firms and of few moments of the distribution that describes the states of fringe firms. Based on this idea we introduce a new equilibrium concept that we call moment-based Markov equilibrium (MME). MME is behaviorally appealing and computationally tractable. However, because moments may not summarize all payoff relevant information, MME strategies may not be optimal. We propose different approaches to overcome this difficulty with varying degrees of restrictions on the model primitives and strategies. We illustrate our methods with computational experiments and show that they work well in empirically relevant models, and significantly extend the class of dynamic oligopoly models that can be studied computationally. In addition, our methods can also be used to improve approximations in other settings such as dynamic industry models with a continuum of firms and an aggregate shock and stochastic growth models.

Suggested Citation

Ifrach, Bar and Weintraub, Gabriel Y., A Framework for Dynamic Oligopoly in Concentrated Industries (August 30, 2012). Columbia Business School Research Paper No. 12/47. Available at SSRN: https://ssrn.com/abstract=2139149 or http://dx.doi.org/10.2139/ssrn.2139149

Bar Ifrach (Contact Author)

Uber Technologies Inc. - Uber Freight ( email )

685 Market Street
San Francisco, CA 94105
United States

Gabriel Y. Weintraub

Stanford Graduate School of Business, Stanford University ( email )

Stanford, CA 94305
United States

Columbia University - Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States

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