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A Framework for Applied Dynamic Analysis in I.O

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  • Ariel Pakes

Abstract

This paper outlines a framework which computes and analyzes the equilibria from a class of dynamic games. The framework dates to Ericson and Pakes (1995), and allows for a finite number of heterogeneous firms, sequential investments with stochastic outcomes, and entry and exit. The equilibrium analyzed is a Markov Perfect equilibrium in the sense of Maskin and Tirole (1988). The simplest version of the framework is supported by a publically accessible computer program which computes equilibrium policies for user-specified primitives, and then analyzes the evolution of the industry from user-specified initial conditions. We begin by outlining the publically accessible framework. It allows for three types of competition in the spot market for current output (specified up to a set of parameter values set by the user), and has modules which allow the user to compare the industry structures generated by the Markov Perfect equilibrium to those that would be generated by a social planner and to those that would be generated by prefect collusion.' Next we review extensions that have been made to the simple framework. These were largely made by other authors who needed to enrich the framework so that it could be used to provide a realistic analysis of particular applied problems. The third section provides a simple way of evaluating the computational burden of the algorithm for a given set of primitives, and then shows that computational constraints are still binding in many applied situations. The last section reviews two computational algorithms designed to alleviate this computational constraint; one of which is based on functional form approximations and the other on learning techniques similar to those used in the artificial intelligence literature.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 8024.

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Date of creation: Dec 2000
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Publication status: published as Handbook of Industrial Organization Volume 3. Elsevier, 2007.
Handle: RePEc:nbr:nberwo:8024

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  1. 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.
  2. Maskin, Eric & Tirole, Jean, 1987. "A theory of dynamic oligopoly, III : Cournot competition," European Economic Review, Elsevier, vol. 31(4), pages 947-968, June.
  3. 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.
  4. Luis M.B. Cabral & Michael Riordan, 1992. "The Learning Curve, Market Dominance and Predatory Pricing," Papers 0039, Boston University - Industry Studies Programme.
  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. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
  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. 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.
  9. 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.
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Cited by:
  1. Yingyao Hu & Matthew Shum, 2008. "Nonparametric Identification of Dynamic Models with Unobserved State Variables," Economics Working Paper Archive 543, The Johns Hopkins University,Department of Economics.
  2. 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.
  3. 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.
  4. Ulrich Doraszelski & Kenneth L. Judd, 2012. "Avoiding the curse of dimensionality in dynamic stochastic games," Quantitative Economics, Econometric Society, vol. 3(1), pages 53-93, 03.
  5. 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.
  6. Doraszelski, Ulrich & Escobar, Juan, 2008. "A Theory of Regular Markov Perfect Equilibria in Dynamic Stochastic Games: Genericity, Stability, and Purification," CEPR Discussion Papers 6805, C.E.P.R. Discussion Papers.
  7. 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.
  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, 2007. "Computable Markov-Perfect Industry Dynamics: Existence, Purification, and Multiplicity," Levine's Bibliography 321307000000000912, UCLA Department of Economics.
  11. 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.
  12. 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.
  13. 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.
  14. Wilson, Nathan E., 2012. "Uncertain regulatory timing and market dynamics," International Journal of Industrial Organization, Elsevier, vol. 30(1), pages 102-115.

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