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Solving Stochastic Dynamic Programming Problems Using Rules Of Thumb

Author

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  • 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 Papers 816, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:816
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_816.pdf
    File Function: First version 1991
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    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.

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