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Discounting long run average growth in stochastic dynamic programs

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  • Duran, Jorge

Abstract

Finding solutions to the Bellman equation relies on restrictive boundedness assumptions. The literature on endogenous growth or business cycle models with unbounded random shocks provide with numerous examples of recursive programs in which returns are not bounded along feasible paths. In this paper we develop a method of proof that allows to account for models of this type. In applications our assumptions only imply that long run average (expected) growth is sufficiently discounted, in sharp contrast with classical assumptons either absolutely bounding growth or bounding each period (instead of long run) maximum (instead of average) growth. We discuss our work in relation to the literature and provide several examples.

Suggested Citation

  • Duran, Jorge, 2001. "Discounting long run average growth in stochastic dynamic programs," CEPREMAP Working Papers (Couverture Orange) 0101, CEPREMAP.
  • Handle: RePEc:cpm:cepmap:0101
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    References listed on IDEAS

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    1. Javier Díaz-Giménez & Vincenzo Quadrini & José-Víctor Ríos-Rull, 1997. "Dimensions of inequality: facts on the U.S. distributions of earnings, income, and wealth," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr, pages 3-21.
    2. Alvarez, Fernando & Stokey, Nancy L., 1998. "Dynamic Programming with Homogeneous Functions," Journal of Economic Theory, Elsevier, vol. 82(1), pages 167-189, September.
    3. Dutta, Jayasri & Michel, Philippe, 1998. "The Distribution of Wealth with Imperfect Altruism," Journal of Economic Theory, Elsevier, vol. 82(2), pages 379-404, October.
    4. Ozaki, Hiroyuki & Streufert, Peter A., 1996. "Dynamic programming for non-additive stochastic objectives," Journal of Mathematical Economics, Elsevier, vol. 25(4), pages 391-442.
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    6. Boud, John III, 1990. "Recursive utility and the Ramsey problem," Journal of Economic Theory, Elsevier, vol. 50(2), pages 326-345, April.
    7. Lucas, Robert Jr. & Stokey, Nancy L., 1984. "Optimal growth with many consumers," Journal of Economic Theory, Elsevier, vol. 32(1), pages 139-171, February.
    8. Ellen R. McGrattan, 1998. "A defense of AK growth models," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 13-27.
    9. Jones, Larry E & Manuelli, Rodolfo E, 1990. "A Convex Model of Equilibrium Growth: Theory and Policy Implications," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 1008-1038, October.
    10. Kreps, David M & Porteus, Evan L, 1978. "Temporal Resolution of Uncertainty and Dynamic Choice Theory," Econometrica, Econometric Society, vol. 46(1), pages 185-200, January.
    11. Streufert, Peter A., 1996. "Biconvergent stochastic dynamic programming, asymptotic impatience, and 'average' growth," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 385-413.
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    Cited by:

    1. Janusz Matkowski & Andrzej Nowak, 2011. "On discounted dynamic programming with unbounded returns," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 46(3), pages 455-474, April.
    2. Kamihigashi, Takashi, 2007. "Stochastic optimal growth with bounded or unbounded utility and with bounded or unbounded shocks," Journal of Mathematical Economics, Elsevier, vol. 43(3-4), pages 477-500, April.
    3. Lores, Francisco Xavier, 2001. "Cyclical behaviour of consumption of non-durable goods: Spain versus U.S.A," UC3M Working papers. Economics we014710, Universidad Carlos III de Madrid. Departamento de Economía.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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