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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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    Cited by:

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    2. Bloise, G. & Van, C. Le & Vailakis, Y., 2024. "An approximation approach to dynamic programming with unbounded returns," Journal of Mathematical Economics, Elsevier, vol. 111(C).
    3. 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.
    4. 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.
    5. Bäuerle, Nicole & Jaśkiewicz, Anna, 2018. "Stochastic optimal growth model with risk sensitive preferences," Journal of Economic Theory, Elsevier, vol. 173(C), pages 181-200.
    6. Paweł Dziewulski, 2011. "On Time-to-Build Economies with Multiple-Stage Investments," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 9, pages 23-49.
    7. Łukasz Balbus & Anna Jaśkiewicz & Andrzej S. Nowak, 2020. "Equilibria in Altruistic Economic Growth Models," Dynamic Games and Applications, Springer, vol. 10(1), pages 1-18, March.
    8. Bloise, Gaetano & Vailakis, Yiannis, 2018. "Convex dynamic programming with (bounded) recursive utility," Journal of Economic Theory, Elsevier, vol. 173(C), pages 118-141.
    9. Alexis Akira Toda, 2024. "Unbounded Markov dynamic programming with weighted supremum norm Perov contractions," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 12(2), pages 141-156, December.

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    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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