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Avoiding the Curse of Dimensionality in Dynamic Stochastic Games

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  • Ulrich Doraszelski
  • Kenneth L. Judd

Abstract

Discrete-time stochastic games with a finite number of states have been widely ap- plied to study the strategic interactions among forward-looking players in dynamic en- vironments. However, these games suffer from a "curse of dimensionality" since the cost of computing players' expectations over all possible future states increases exponentially in the number of state variables. We explore the alternative of continuous-time stochas- tic games with a finite number of states, and show that continuous time has substantial computational and conceptual advantages. Most important, continuous time avoids the curse of dimensionality, thereby speeding up the computations by orders of magnitude in games with more than a few state variables. Overall, the continuous-time approach opens the way to analyze more complex and realistic stochastic games than currently feasible.

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Bibliographic Info

Paper provided by Harvard - Institute of Economic Research in its series Harvard Institute of Economic Research Working Papers with number 2059.

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Date of creation: 2005
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Handle: RePEc:fth:harver:2059

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  1. Ariel Pakes & Michael Ostrovsky & Steve Berry, 2004. "Simple Estimators for the Parameters of Discrete Dynamic Games (with Entry/Exit Samples)," NBER Working Papers 10506, National Bureau of Economic Research, Inc.
  2. Caplin, A. & Nalebuff, B., 1989. "Aggregation And Imperfect Competition: On The Existence Of Equilibrium," Discussion Papers 1989_30, Columbia University, Department of Economics.
  3. David Besanko & Ulrich Doraszelski, 2002. "Capacity Dynamics and Endogenous Asymmetries in Firm Size," Computing in Economics and Finance 2002 196, Society for Computational Economics.
  4. Ronald L. Goettler & Christine A. Parlour, 2004. "Equilibrium in a Dynamic Limit Order Market," 2004 Meeting Papers 757, Society for Economic Dynamics.
  5. J. Levin & P. Bajari, 2004. "Estimating Dynamic Models of Imperfect Competition," 2004 Meeting Papers 579, Society for Economic Dynamics.
  6. Pesendorfer, Martin & Schmidt-Dengler, Philipp, 2003. "Identification and Estimation of Dynamic Games," CEPR Discussion Papers 3965, C.E.P.R. Discussion Papers.
  7. Ariel Pakes & Paul McGuire, 1992. "Computing Markov perfect Nash equilibria: numerical implications of a dynamic differentiated product model," Discussion Paper / Institute for Empirical Macroeconomics 58, Federal Reserve Bank of Minneapolis.
  8. Fershtman, C. & Pakes, A., 1998. "A Dynamic Oligopoly with Collusion and Price Wars," Papers 26-98, Tel Aviv.
  9. C. Lanier Benkard, 2004. "A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft," Review of Economic Studies, Wiley Blackwell, vol. 71, pages 581-611, 07.
  10. Dockner,Engelbert J. & Jorgensen,Steffen & Long,Ngo Van & Sorger,Gerhard, 2000. "Differential Games in Economics and Management Science," Cambridge Books, Cambridge University Press, number 9780521637329.
  11. 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.
  12. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711.
  13. Ulrich Doraszelski & Sarit Markovich, 2004. "Advertising Dynamics and Competitive Advantage," Computing in Economics and Finance 2004 61, Society for Computational Economics.
  14. Pakes, Ariel & McGuire, Paul, 2001. "Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the 'Curse' of Dimensionality," Econometrica, Econometric Society, vol. 69(5), pages 1261-81, September.
  15. Ulrich Doraszelski & Sarit Markovich, 2004. "Advertising Dynamics and Competitive Advantage," Econometric Society 2004 North American Summer Meetings 162, Econometric Society.
  16. Patricia Langohr, 2003. "Competitive Convergence and Divergence: Capability and Position Dynamics," Computing in Economics and Finance 2003 229, Society for Computational Economics.
  17. Gowrisankaran, Gautam, 1999. "Efficient representation of state spaces for some dynamic models," Journal of Economic Dynamics and Control, Elsevier, vol. 23(8), pages 1077-1098, August.
  18. Ariel Pakes, 2000. "A Framework for Applied Dynamic Analysis in I.O," NBER Working Papers 8024, National Bureau of Economic Research, Inc.
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Cited by:
  1. Besanko, David & Doraszelski, Ulrich & Kryukov, Yaroslav & Satterthwaite, Mark, 2007. "Learning-by-Doing, Organizational Forgetting and Industry Dynamics," CEPR Discussion Papers 6160, C.E.P.R. Discussion Papers.
  2. Pichler, Paul, 2011. "Solving the multi-country Real Business Cycle model using a monomial rule Galerkin method," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 240-251, February.

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