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Markov Perfect Industry Dynamics with Many Firms

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  • Gabriel Weintraub
  • C. Lanier Benkard
  • Ben Van Roy
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    Abstract

    We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We develop a simple algorithm for computing an ``oblivious equilibrium,'' in which each firm is assumed to make decisions based only on its own state and knowledge of the long run average industry state, but where firms ignore current information about competitors' states. We prove that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain ``light-tail'' condition, then oblivious equilibria closely approximate Markov perfect equilibria. We develop bounds that can be computed to assess the accuracy of the approximation for any given applied problem. Through computational experiments, we find that the method often generates useful approximations for industries with hundreds of firms and in some cases even tens of firms.

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    Bibliographic Info

    Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11900.

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    Date of creation: Dec 2005
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    Publication status: published as Weintraub, Gabriel, C. Lanier Benkard and Ben Van Roy. "Markov Perfect Industry Dynamics with Many Firms." Econometrica (Nov 2008): 1375-1411.
    Handle: RePEc:nbr:nberwo:11900

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    1. Ronald L. Goettler & Christine A. Parlour, 2004. "Equilibrium in a Dynamic Limit Order Market," 2004 Meeting Papers 757, Society for Economic Dynamics.
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    16. de Roos, Nicolas, 2004. "A model of collusion timing," International Journal of Industrial Organization, Elsevier, vol. 22(3), pages 351-387, March.
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    Cited by:
    1. Xiao, Junji, 2008. "Markov Perfect Equilibrium in the US digital camera market," International Journal of Industrial Organization, Elsevier, vol. 26(5), pages 1233-1249, September.
    2. Weintraub, Gabriel Y. & Benkard, C. Lanier & Van Roy, Benjamin, 2007. "Computational Methods for Oblivious Equilibrium," Research Papers 1969, Stanford University, Graduate School of Business.

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