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Perov's Contraction Principle and Dynamic Programming with Stochastic Discounting

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  • Alexis Akira Toda

Abstract

This paper shows the usefulness of Perov's contraction principle, which generalizes Banach's contraction principle to a vector-valued metric, for studying dynamic programming problems in which the discount factor can be stochastic. The discounting condition $\beta

Suggested Citation

  • Alexis Akira Toda, 2021. "Perov's Contraction Principle and Dynamic Programming with Stochastic Discounting," Papers 2103.14173, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2103.14173
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    File URL: http://arxiv.org/pdf/2103.14173
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    References listed on IDEAS

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    1. Harrison, J. Michael & Kreps, David M., 1979. "Martingales and arbitrage in multiperiod securities markets," Journal of Economic Theory, Elsevier, vol. 20(3), pages 381-408, June.
    2. Per Krusell & Anthony A. Smith & Jr., 1998. "Income and Wealth Heterogeneity in the Macroeconomy," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 867-896, October.
    3. Stachurski, John & Zhang, Junnan, 2021. "Dynamic programming with state-dependent discounting," Journal of Economic Theory, Elsevier, vol. 192(C).
    4. Cao, Dan, 2020. "Recursive equilibrium in Krusell and Smith (1998)," Journal of Economic Theory, Elsevier, vol. 186(C).
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    Cited by:

    1. Alexis Akira Toda, 2023. "Unbounded Markov Dynamic Programming with Weighted Supremum Norm Perov Contractions," Papers 2310.04593, arXiv.org.

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