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Stability under learning: the neo-classical growth problem

Author

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  • Orlando Gomes

    (ISCAL - IPL; Economics Research Center [UNIDE/ISCTE - ERC])

Abstract

A local stability condition for the standard neo-classical Ramsey growth model is derived. The proposed setting is deterministic, defined in discrete time and expectations are formed through adaptive learning. The stability condition imposes an upper bound on the long-term value of the gain sequence.

Suggested Citation

  • Orlando Gomes, 2009. "Stability under learning: the neo-classical growth problem," Economics Bulletin, AccessEcon, vol. 29(4), pages 3186-3193.
    • Handle: RePEc:ebl:ecbull:eb-09-00718
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    References listed on IDEAS

    as
    1. Stefano Eusepi & Bruce Preston, 2011. "Expectations, Learning, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 101(6), pages 2844-2872, October.
    2. KevinX.D. Huang & Zheng Liu & Tao Zha, 2009. "Learning, Adaptive Expectations and Technology Shocks," Economic Journal, Royal Economic Society, vol. 119(536), pages 377-405, March.
    3. Klaus Adam & Albert Marcet & Juan Pablo Nicolini, 2016. "Stock Market Volatility and Learning," Journal of Finance, American Finance Association, vol. 71(1), pages 33-82, February.
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    7. Sobel, Joel, 2000. "Economists' Models of Learning," Journal of Economic Theory, Elsevier, vol. 94(2), pages 241-261, October.
    8. Gomes, Orlando, 2009. "Stability under learning: The endogenous growth problem," Economic Modelling, Elsevier, vol. 26(5), pages 807-816, September.
    9. Bullard James, 1994. "Learning Equilibria," Journal of Economic Theory, Elsevier, vol. 64(2), pages 468-485, December.
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    More about this item

    Keywords

    Neo-classical growth; Adaptive learning; Stability;
    All these keywords.

    JEL classification:

    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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