IDEAS home Printed from https://ideas.repec.org/p/qed/wpaper/816.html
   My bibliography  Save this paper

Solving Stochastic Dynamic Programming Problems Using Rules Of Thumb

Author

Listed:
  • Anthony A. Smith, Jr.

Abstract

This paper develops a new method for constructing approximate solutions to discrete time, infinite horizon, discounted stochastic dynamic programming problems with convex choice sets. The key idea is to restrict the decision rule to belong to a parametric class of function. The agent then chooses the best decision rule from within this class. Monte Carlo simulations are used to calculate arbitrarily precise estimates of the optimal decision rule parameters. The solution method is used to solve a version of the Brock-Mirman (1972) stochastic optimal growth model. For this model, relatively simple rules of thumb provide very good approximations to optimal behavior.

Suggested Citation

  • Anthony A. Smith, Jr., 1991. "Solving Stochastic Dynamic Programming Problems Using Rules Of Thumb," Working Paper 816, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:816
    as

    Download full text from publisher

    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_816.pdf
    File Function: First version 1991
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arellano, Cristina & Maliar, Lilia & Maliar, Serguei & Tsyrennikov, Viktor, 2016. "Envelope condition method with an application to default risk models," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 436-459.
    2. Letendre, Marc-Andre & Smith, Gregor W., 2001. "Precautionary saving and portfolio allocation: DP by GMM," Journal of Monetary Economics, Elsevier, vol. 48(1), pages 197-215, August.
    3. Judd, Kenneth L. & Maliar, Lilia & Maliar, Serguei & Valero, Rafael, 2014. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 92-123.
    4. Geweke, John, 1996. "Monte carlo simulation and numerical integration," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 15, pages 731-800, Elsevier.
    5. Richard, Jean-François, 2000. "Conférence François-Albert Angers (1999). Enchères : théorie économique et réalité," L'Actualité Economique, Société Canadienne de Science Economique, vol. 76(2), pages 173-198, juin.
    6. Garcia, Diego, 2003. "Convergence and Biases of Monte Carlo estimates of American option prices using a parametric exercise rule," Journal of Economic Dynamics and Control, Elsevier, vol. 27(10), pages 1855-1879, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qed:wpaper:816. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark Babcock (email available below). General contact details of provider: https://edirc.repec.org/data/qedquca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.