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Hybrid Methods for Continuous Space Dynamic Programming

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Author Info

  • Mario J. Miranda

    ()
    (The Ohio State University)

  • Paul L. Fackler

    ()
    (North Carolina State University)

Abstract

We propose a method for solving continuous-state and action-stochastic dynamic programs that is a hybrid between the continuous space projection methods introduced by Judd and the discrete space methods introduced by Bellman. Our hybrid approach yields a smooth representation of the value function while preserving the computational simplicity of discrete dynamic programming. Our method is especially well suited for implementation in a vector processing environment such as MATLAB or GAUSS, and makes it possible to automate the setup and solution of continuous space dynamic programs in a way that previously seemed elusive.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 1332.

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Date of creation: 01 Mar 1999
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Handle: RePEc:sce:scecf9:1332

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Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA
Fax: +1-617-552-2308
Web page: http://fmwww.bc.edu/CEF99/
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Cited by:
  1. King, Robert P. & Lohano, Heman D., 2006. "Accuracy of Numerical Solution to Dynamic Programming Models," Staff Papers 14230, University of Minnesota, Department of Applied Economics.
  2. Sayan, Serdar & Kiraci, Arzdar, 2001. "Parametric pension reform with higher retirement ages: A computational investigation of alternatives for a pay-as-you-go-based pension system," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 951-966, June.
  3. Msangi, Siwa, 2005. "Measuring the Gains to Groundwater Management with Recursive Utility," 2005 Annual meeting, July 24-27, Providence, RI 19212, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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