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Solving Nonlinear Dynamic Stochastic Models: An Algorithm Computing Value Functions By Simulations

Author

Listed:
  • Lilia Maliar

    (Universidad de Alicante)

  • Serguei Maliar

    (Universidad de Alicante)

Abstract

This paper presents an algorithm for solving nonlinear dynamic stochastic models that computes value function by simulations. We argue that the proposed algorithm can be a useful alternative to the existing methods in some applications.

Suggested Citation

  • Lilia Maliar & Serguei Maliar, 2004. "Solving Nonlinear Dynamic Stochastic Models: An Algorithm Computing Value Functions By Simulations," Working Papers. Serie AD 2004-37, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    • Handle: RePEc:ivi:wpasad:2004-37
    as

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    File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2004-37.pdf
    File Function: Fisrt version / Primera version, 2004
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    References listed on IDEAS

    as
    1. Christiano, Lawrence J. & Fisher, Jonas D. M., 2000. "Algorithms for solving dynamic models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 24(8), pages 1179-1232, July.
    2. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
    3. David Andolfatto & Glenn MacDonald, 1998. "Technology Diffusion and Aggregate Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 1(2), pages 338-370, April.
    4. Maliar, Lilia & Maliar, Serguei, 2003. "Parameterized Expectations Algorithm and the Moving Bounds," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 88-92, January.
    5. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729, Elsevier.
    6. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    7. Albert Marcet & Guido Lorenzoni, 1998. "Parameterized expectations approach; Some practical issues," Economics Working Papers 296, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Scott Freeman & Dong-Pyo Hong & Dan Peled, 1999. "Endogenous Cycles and Growth with Indivisible Technological Developments," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 2(2), pages 402-432, April.
    9. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
    10. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979, Decembrie.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex Business School.
    2. Kenneth Judd & Lilia Maliar & Serguei Maliar, 2009. "Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models," NBER Working Papers 15296, National Bureau of Economic Research, Inc.
    3. Kenneth L. Judd & Lilia Maliar & Serguei Maliar & Inna Tsener, 2017. "How to solve dynamic stochastic models computing expectations just once," Quantitative Economics, Econometric Society, vol. 8(3), pages 851-893, November.
    4. John Stachurski, 2009. "Economic Dynamics: Theory and Computation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012774, December.

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    More about this item

    Keywords

    Nonlinear stochastic models; Value function; Parameterized expectations; Monte Carlo simulations; Numerical solutions;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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