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How to Solve Dynamic Stochastic Models Computing Expectations Just Once

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  • Kenneth L. Judd
  • Lilia Maliar
  • Serguei Maliar

Abstract

We introduce a technique called "precomputation of integrals" that makes it possible to compute conditional expectations in dynamic stochastic models in the initial stage of the solution procedure. This technique can be applied to any set of equations that contains conditional expectations, in particular, to the Bellman and Euler equations. After the integrals are precomputed, we can solve stochastic models as if they were deterministic. We illustrate the benefits of precomputation of integrals using one- and multi-agent numerical examples.

Suggested Citation

  • Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "How to Solve Dynamic Stochastic Models Computing Expectations Just Once," NBER Working Papers 17418, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17418
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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. Kollmann, Robert & Maliar, Serguei & Malin, Benjamin A. & Pichler, Paul, 2011. "Comparison of solutions to the multi-country Real Business Cycle model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 186-202, February.
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    6. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models," Quantitative Economics, Econometric Society, vol. 2(2), pages 173-210, July.
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    11. Den Haan, Wouter J., 2010. "Comparison of solutions to the incomplete markets model with aggregate uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 34(1), pages 4-27, January.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. How to Solve Dynamic Stochastic Models Computing Expectations Just Once
      by Christian Zimmermann in NEP-DGE blog on 2011-10-24 08:00:06

    Citations

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    Cited by:

    1. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    2. Yasuo Hirose & Takeki Sunakawa, 2019. "Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound," The Japanese Economic Review, Springer, vol. 70(1), pages 51-104, March.
    3. Ivan Rudik & Derek Lemoine & Maxwell Rosenthal, 2018. "General Bayesian Learning in Dynamic Stochastic Models: Estimating the Value of Science Policy," 2018 Meeting Papers 369, Society for Economic Dynamics.
    4. Hull, Isaiah, 2015. "Approximate dynamic programming with post-decision states as a solution method for dynamic economic models," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 57-70.
    5. Lilia Maliar & Serguei Maliar & Sébastien Villemot, 2013. "Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions," Computational Economics, Springer;Society for Computational Economics, vol. 42(3), pages 307-325, October.
    6. Gary S. Anderson, 2018. "Reliably Computing Nonlinear Dynamic Stochastic Model Solutions: An Algorithm with Error Formulas," Finance and Economics Discussion Series 2018-070, Board of Governors of the Federal Reserve System (U.S.).
    7. Thomas H. Jørgensen & Maxime Tô, 2020. "Robust Estimation of Finite Horizon Dynamic Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 499-509, February.
    8. Fabian Goessling, 2019. "Exact Expectations: Efficient Calculation of DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 977-990, March.
    9. Rubini, Loris & Moro, Alessio, 2019. "Stochastic Structural Change," MPRA Paper 96144, University Library of Munich, Germany.
    10. Ayse Kabukcuoglu & Enrique Martinez-Garcia, 2020. "A Generalized Time Iteration Method for Solving Dynamic Optimization Problems with Occasionally Binding Constraints," Globalization Institute Working Papers 396, Federal Reserve Bank of Dallas.

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    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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