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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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17418.

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Date of creation: Sep 2011
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Handle: RePEc:nbr:nberwo:17418

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  1. Maliar, Serguei & Maliar, Lilia & Judd, Kenneth, 2011. "Solving the multi-country real business cycle model using ergodic set methods," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 35(2), pages 207-228, February.
  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, Elsevier, vol. 35(2), pages 186-202, February.
  3. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, SUNY-Oswego, Department of Economics, number comp1, Spring.
  4. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 30(12), pages 2477-2508, December.
  5. Lawrence J. Christiano & Jonas D.M. Fisher, 1994. "Algorithms for solving dynamic models with occasionally binding constraints," Working Paper Series, Macroeconomic Issues, Federal Reserve Bank of Chicago 94-6, Federal Reserve Bank of Chicago.
  6. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, The MIT Press, edition 1, volume 1, number 0262100711, December.
  7. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models," Quantitative Economics, Econometric Society, Econometric Society, vol. 2(2), pages 173-210, 07.
  8. Den Haan, Wouter, 2008. "Comparison of Solutions to the Incomplete Markets Model with Aggregate Uncertainty," CEPR Discussion Papers, C.E.P.R. Discussion Papers 7019, C.E.P.R. Discussion Papers.
  9. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, Elsevier, vol. 58(2), pages 410-452, December.
  10. Gaspar, Jess & L. Judd, Kenneth, 1997. "Solving Large-Scale Rational-Expectations Models," Macroeconomic Dynamics, Cambridge University Press, Cambridge University Press, vol. 1(01), pages 45-75, January.
  11. Francisco Barillas & Jesús Fernández-Villaverde, 2006. "A Generalization of the Endogenous Grid Method," Levine's Bibliography 122247000000001200, UCLA Department of Economics.
  12. Maliar, Lilia & Maliar, Serguei, 2005. "Solving nonlinear dynamic stochastic models: an algorithm computing value function by simulations," Economics Letters, Elsevier, Elsevier, vol. 87(1), pages 135-140, April.
  13. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2010. "A Cluster-Grid Projection Method: Solving Problems with High Dimensionality," NBER Working Papers 15965, National Bureau of Economic Research, Inc.
  14. Miranda, Mario J & Helmberger, Peter G, 1988. "The Effects of Commodity Price Stabilization Programs," American Economic Review, American Economic Association, American Economic Association, vol. 78(1), pages 46-58, March.
  15. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198294979, October.
  16. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, Elsevier, vol. 20(2), pages 177-181.
  17. Krueger, Dirk & Kubler, Felix, 2004. "Computing equilibrium in OLG models with stochastic production," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 28(7), pages 1411-1436, April.
  18. Albert Marcet & Guido Lorenzoni, 1998. "The Parameterized Expectations Approach: Some Practical Issues," QM&RBC Codes, Quantitative Macroeconomics & Real Business Cycles 128, Quantitative Macroeconomics & Real Business Cycles.
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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 03:00:06
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Cited by:
  1. Lilia Maliar & Serguei Maliar & Sébastien Villemot, 2013. "Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 42(3), pages 307-325, October.

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