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Computational Methods for Oblivious Equilibrium

  • Weintraub, Gabriel Y.

    (Columbia U)

  • Benkard, C. Lanier

    (Stanford U)

  • Van Roy, Benjamin
Registered author(s):

    Oblivious equilibrium is a new solution concept for approximating Markov perfect equilibrium in dynamic models of imperfect competition among heterogeneous firms and has recently been used in multiple economic studies. In this paper, we present algorithms for computing oblivious equilibrium and for bounding approximation error. We report results from computational case studies that serve to assess both efficiency of the algorithms and accuracy of oblivious equilibrium as an approximation to Markov perfect equilibrium. We also extend the definition of oblivious equilibrium, originally proposed for models with only firm-specific idiosyncratic random shocks, and our algorithms to accommodate models with industry-wide aggregate shocks. Our results suggest that, by using oblivious equilibrium to approximate Markov perfect equilibrium, it is possible to greatly increase the set of dynamic models of imperfect competition that can be analyzed computationally.

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    Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1969.

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    Date of creation: Apr 2007
    Date of revision:
    Handle: RePEc:ecl:stabus:1969
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    1. Jonathan Levin (Stanford University) & Pat Bajari & Lanier Benkard, 2004. "Estimating Dynamic Models of Imperfect Competition," Econometric Society 2004 North American Winter Meetings 627, Econometric Society.
    2. Victor Aguirregabiria & Pedro Mira, 2004. "Sequential Estimation Of Dynamic Discrete Games," Working Papers wp2004_0413, CEMFI.
    3. 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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    8. Kenneth Judd & Karl Schmedders & Sevin Yeltekin, . "Optimal Rules for Patent Races," GSIA Working Papers 2006-E37, Carnegie Mellon University, Tepper School of Business.
    9. Schivardi, Fabiano & Schneider, Martin, 2005. "Strategic Experimentation and Disruptive Technological Change," CEPR Discussion Papers 4925, C.E.P.R. Discussion Papers.
    10. Minjae Song, 2011. "A Dynamic Analysis Of Cooperative Research In The Semiconductor Industry," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(4), pages 1157-1177, November.
    11. Ariel Pakes & Michael Ostrovsky & Steven Berry, 2007. "Simple estimators for the parameters of discrete dynamic games (with entry/exit examples)," RAND Journal of Economics, RAND Corporation, vol. 38(2), pages 373-399, 06.
    12. Gabriel Y. Weintraub & C. Lanier Benkard & Benjamin Van Roy, 2005. "Markov perfect industry dynamics with many firms," Working Paper Series 2005-23, Federal Reserve Bank of San Francisco.
    13. Fershtman, Chaim & Pakes, Ariel, 2005. "Finite State Dynamic Games with Asymmetric Information: A Framework for Applied Work," CEPR Discussion Papers 5024, C.E.P.R. Discussion Papers.
    14. David Besanko & Ulrich Doraszelski, 2004. "Capacity Dynamics and Endogenous Asymmetries in Firm Size," RAND Journal of Economics, The RAND Corporation, vol. 35(1), pages 23-49, Spring.
    15. Berry, Steven & Pakes, Ariel, 1993. "Some Applications and Limitations of Recent Advances in Empirical Industrial Organization: Merger Analysis," American Economic Review, American Economic Association, vol. 83(2), pages 247-52, May.
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    17. Ronald L. Goettler & Christine A. Parlour, 2004. "Equilibrium in a Dynamic Limit Order Market," 2004 Meeting Papers 757, Society for Economic Dynamics.
    18. Ericson, Richard & Pakes, Ariel, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," Review of Economic Studies, Wiley Blackwell, vol. 62(1), pages 53-82, January.
    19. Gabriel Weintraub & C. Lanier Benkard & Ben Van Roy, 2005. "Markov Perfect Industry Dynamics with Many Firms," NBER Working Papers 11900, National Bureau of Economic Research, Inc.
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