Solving Stochastic Dynamic Programming Problems Using Rules Of Thumb
AbstractThis 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.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 816.
Length: 36 pages
Date of creation: May 1991
Date of revision:
rule of thumb; Monte Carlo simulation; numerical optimization;
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- Marc-Andre Letendre & Gregor Smith, 2000.
"Precautionary saving and portfolio allocation: DP by GMM,"
1247, Queen's University, Department of Economics.
- 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.
- Kenneth L. Judd & Lilia Maliar & Serguei Maliar & Rafael Valero, 2013.
"Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain,"
NBER Working Papers
19326, National Bureau of Economic Research, Inc.
- Kenneth Judd & Lilia Maliar & Rafael Valero & Serguei Maliar, 2013. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Working Papers. Serie AD 2013-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Kenneth L. Judd & Lilia Maliar & Serguei Maliar & Rafael Valero, 2013. "Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain," BYU Macroeconomics and Computational Laboratory Working Paper Series 2013-02, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
- 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
- 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.
- 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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