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A Framework for Applied Dynamic Analysis in IO

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  • Doraszelski, Ulrich
  • Pakes, Ariel

Abstract

This paper reviews a framework for numerically analyzing dynamic interactions in imperfectly competitive industries. The framework dates back to Ericson and Pakes [1995. Review of Economic Studies 62, 53-82], but it is based on equilibrium notions that had been available for some time before, and it has been extended in many ways by different authors since. The framework requires as input a set of primitives which describe the institutional structure in the industry to be analyzed. The framework outputs profits and policies for every incumbent and potential entrant at each possible state of the industry. These policies can be used to simulate the distribution of sample paths for all firms from any initial industry structure. The sample paths generated by the model can be quite different depending on the primitives, and most of the extensions were designed to enable the framework to accommodate empirically relevant cases that required modification of the initial structure. The sample paths possess similar properties to those observed in (the recently available) panel data sets on industries. These sample paths can be used either for an analysis of the likely response to a policy or an environmental change, or as the model's implication in an estimation algorithm. We begin with a review of an elementary version of the framework and a report on what is known about its analytic properties. Much of the rest of the paper deals with computational issues. We start with an introduction to iterative techniques for computing equilibrium that are analogous to the techniques used to compute the solution to single agent dynamic programming problems. This includes discussions of the determinants of the computational burden of these techniques, and the mechanism implicitly used to select an equilibrium when multiple equilibria are possible. We then outline a number of techniques that might be used to reduce the computational burden of the iterative algorithm. This section includes discussions of both the implications of differences in modeling assumptions used in the alternative techniques, and a discussion of the likely relevance of the different techniques for different institutional structures. A separate section reports on a technique for computing multiple equilibria from the same set of primitives. The paper concludes with a review of applications of the framework and a brief discussion of areas where further development of the framework would seem warranted.

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This chapter was published in:

  • Mark Armstrong & Robert Porter (ed.), 2007. "Handbook of Industrial Organization," Handbook of Industrial Organization, Elsevier, edition 1, volume 3, number 1, 00.
    This item is provided by Elsevier in its series Handbook of Industrial Organization with number 3-30.

    Handle: RePEc:eee:indchp:3-30

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    Keywords: industrial organization;

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    References

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    1. Maskin, Eric & Tirole, Jean, 1987. "A theory of dynamic oligopoly, III : Cournot competition," European Economic Review, Elsevier, vol. 31(4), pages 947-968, June.
    2. Maskin, Eric & Tirole, Jean, 1988. "A Theory of Dynamic Oligopoly, II: Price Competition, Kinked Demand Curves, and Edgeworth Cycles," Econometrica, Econometric Society, vol. 56(3), pages 571-99, May.
    3. Luis M.B. Cabral & Michael Riordan, 1992. "The Learning Curve, Market Dominance and Predatory Pricing," Papers 0039, Boston University - Industry Studies Programme.
    4. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
    5. Jofre-Bonet, Mireia & Pesendorfer, Martin, 2000. "Bidding behavior in a repeated procurement auction: A summary," European Economic Review, Elsevier, vol. 44(4-6), pages 1006-1020, May.
    6. Gautam Gowrisankaran & Robert J. Town, 1997. "Dynamic Equilibrium in the Hospital Industry," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 6(1), pages 45-74, 03.
    7. Abreu, Dilip & Pearce, David & Stacchetti, Ennio, 1986. "Optimal cartel equilibria with imperfect monitoring," Journal of Economic Theory, Elsevier, vol. 39(1), pages 251-269, June.
    8. C. Lanier Benkard, 2000. "Learning and Forgetting: The Dynamics of Aircraft Production," American Economic Review, American Economic Association, vol. 90(4), pages 1034-1054, September.
    9. 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.
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    Cited by:
    1. Hu, Yingyao & Shum, Matthew, 2012. "Nonparametric identification of dynamic models with unobserved state variables," Journal of Econometrics, Elsevier, vol. 171(1), pages 32-44.
    2. Ulrich Doraszelski & Kenneth L. Judd, 2005. "Avoiding the Curse of Dimensionality in Dynamic Stochastic Games," NBER Technical Working Papers 0304, National Bureau of Economic Research, Inc.
    3. 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.
    4. Ulrich Doraszelski & Mark Satterthwaite, 2007. "Computable Markov-Perfect Industry Dynamics: Existence, Purification, and Multiplicity," Levine's Bibliography 321307000000000912, UCLA Department of Economics.
    5. Noel, Michael D., 2004. "Edgeworth Cycles and Focal Prices: Computational Dynamic Markov Equilibria," University of California at San Diego, Economics Working Paper Series qt59t3g818, Department of Economics, UC San Diego.
    6. Cerquera Dussán, Daniel, 2006. "Dynamic R&D incentives with network externalities," ZEW Discussion Papers 06-94, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    7. Doraszelski, Ulrich & Escobar, Juan, 2010. "A theory of regular Markov perfect equilibria in dynamic stochastic games: genericity, stability, and purification," Theoretical Economics, Econometric Society, vol. 5(3), September.
    8. 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.
    9. Cabral, Luís, 2012. "Oligopoly Dynamics," International Journal of Industrial Organization, Elsevier, vol. 30(3), pages 278-282.
    10. Ulrich Doraszelski & Mark Satterthwaite, 2003. "Foundations of Markov-Perfect Industry Dynamics. Existence, Purification, and Multiplicity," Discussion Papers 1383, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    11. Narajabad, Borghan & Watson, Randal, 2011. "The dynamics of innovation and horizontal differentiation," Journal of Economic Dynamics and Control, Elsevier, vol. 35(6), pages 825-842, June.
    12. Szabolcs Lorincz, 2005. "Persistence Effects in a Dynamic Discrete Choice Model - Application to Low-End Computer Servers," IEHAS Discussion Papers 0510, Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences.
    13. Wilson, Nathan E., 2012. "Uncertain regulatory timing and market dynamics," International Journal of Industrial Organization, Elsevier, vol. 30(1), pages 102-115.
    14. Nevo, Aviv & Rossi, Federico, 2008. "An approach for extending dynamic models to settings with multi-product firms," Economics Letters, Elsevier, vol. 100(1), pages 49-52, July.

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