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Solving nonlinear dynamic stochastic models: an algorithm computing value function by simulations

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  • Maliar, Lilia
  • Maliar, Serguei

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.
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Suggested Citation

  • Maliar, Lilia & Maliar, Serguei, 2005. "Solving nonlinear dynamic stochastic models: an algorithm computing value function by simulations," Economics Letters, Elsevier, vol. 87(1), pages 135-140, April.
  • Handle: RePEc:eee:ecolet:v:87:y:2005:i:1:p:135-140
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    References listed on IDEAS

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    1. repec:fth:simfra:95-08 is not listed on IDEAS
    2. repec:fth:waterl:9503 is not listed on IDEAS
    3. 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.
    4. 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.
    5. 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.
    6. Albert Marcet & Guido Lorenzoni, 1998. "The Parameterized Expectations Approach: Some Practical Issues," QM&RBC Codes 128, Quantitative Macroeconomics & Real Business Cycles.
    7. 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.
    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, March.
    10. 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.
    11. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979.
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    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.
    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, January.

    More about this item

    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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