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On capital accumulation paths in a neoclassical stochastic growth model

Author

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  • Kaushik Mitra

    (Department of Economics, Uris Hall, Cornell University, Ithaca, NY 14853, USA)

Abstract

Boldrin and Montrucchio [2] showed that any twice continuously differentiable function could be obtained as the optimal policy function for some value of the discount parameter in a deterministic neoclassical growth model. I extend their result to the stochastic growth model with non-degenerate shocks to preferences or technology. This indicates that one can obtain complex dynamics endogenously in a wide variety of economic models, both under certainty and uncertainty. Further, this result motivates the analysis of convergence of adaptive learning mechanisms to rational expectations in economic models with (potentially) complicated dynamics.

Suggested Citation

  • Kaushik Mitra, 1998. "On capital accumulation paths in a neoclassical stochastic growth model," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 11(2), pages 457-464.
  • Handle: RePEc:spr:joecth:v:11:y:1998:i:2:p:457-464
    Note: Received: June 21, 1996; revised version: October 31, 1996
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    Citations

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    Cited by:

    1. Santanu Roy & Itzhak Zilcha, 2012. "Stochastic growth with short-run prediction of shocks," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 51(3), pages 539-580, November.
    2. Lars J. Olson & Santanu Roy, 2006. "Theory of Stochastic Optimal Economic Growth," Springer Books, in: Rose-Anne Dana & Cuong Le Van & Tapan Mitra & Kazuo Nishimura (ed.), Handbook on Optimal Growth 1, chapter 11, pages 297-335, Springer.
    3. repec:cte:werepe:35342 is not listed on IDEAS
    4. Juan Pablo Rinc'on-Zapatero, 2019. "Existence and Uniqueness of Solutions to the Stochastic Bellman Equation with Unbounded Shock," Papers 1907.07343, arXiv.org.

    More about this item

    JEL classification:

    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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