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Parametrized Expectations Algorithm And The Moving Bounds

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  • Lilia Maliar

    ()
    (Universidad de Alicante)

  • Serguei Maliar

    (Universidad de Alicante)

Abstract

Parametrized Expectation Algorithm (PEA) is a powerful tool for solving nonlinear stochastic dynamic models. However, it has an important shortcoming: it is not a contraction mapping technique and thus, does not guarantee finding the solution. We suggest a simple modification that enhances the convergence property of the algorithm. The idea is to rule out the possibility of (ex)implosive behavior by artificially restricting the simulated series within certain bounds. As the solution is refined along the iterations, the bounds are gradually removed. The modified PEA can systematically converge to the stationary solution starting from the nonstochastic steady state.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2001-23.pdf
File Function: Fisrt version / Primera version, 2001
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Bibliographic Info

Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2001-23.

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Length: 19 pages
Date of creation: Aug 2001
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2001-23

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Keywords: Parametrized expectations algorithm; Nonlinear models;

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References

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  1. 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.
  2. Lawrence J. Christiano & Jonas D.M. Fisher, 1994. "Algorithms for solving dynamic models with occasionally binding constraints," Working Paper Series, Macroeconomic Issues 94-6, Federal Reserve Bank of Chicago.
  3. Wouter J. den Haan & Albert Marcet, 1993. "Accuracy in simulations," Economics Working Papers 42, Department of Economics and Business, Universitat Pompeu Fabra.
  4. Miranda, Mario J & Helmberger, Peter G, 1988. "The Effects of Commodity Price Stabilization Programs," American Economic Review, American Economic Association, vol. 78(1), pages 46-58, March.
  5. Wright, Brian D & Williams, Jeffrey C, 1982. "The Economic Role of Commodity Storage," Economic Journal, Royal Economic Society, vol. 92(367), pages 596-614, September.
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Citations

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Cited by:
  1. Paul Pichler, 2005. "Evaluating Approximate Equilibria of Dynamic Economic Models," Vienna Economics Papers 0510, University of Vienna, Department of Economics.
  2. Lilia Maliar & Serguei Maliar, 2005. "Parameterized Expectations Algorithm: How to Solve for Labor Easily," Computational Economics, Society for Computational Economics, vol. 25(3), pages 269-274, June.
  3. Rhys Bidder & Kalin Nikolov & Tony Yates, . " Self-confirming Inflation Persistence," CDMA Conference Paper Series 0908, Centre for Dynamic Macroeconomic Analysis.
  4. Ángel Gavilán & Juan A. Rojas, 2009. "Solving Portfolio Problems with the Smolyak-Parameterized Expectations Algorithm," Banco de Espa�a Working Papers 0838, Banco de Espa�a.
  5. Lilia Maliar & Serguei Maliar, 2006. "Capital-Skill Complementarity And Steady-State Growth," Working Papers. Serie AD 2006-15, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  6. Lilia Maliar & Serguei Maliar, 2004. "Preference shocks from aggregation: time series data evidence," Canadian Journal of Economics, Canadian Economics Association, vol. 37(3), pages 768-781, August.
  7. 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).
  8. Pérez, Javier J. & Sánchez, A. Jesús, 2009. "Alternatives to initialize the Parameterized Expectations Algorithm," Economics Letters, Elsevier, vol. 102(2), pages 116-118, February.
  9. 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.
  10. Lilia Maliar & Fernando Valli & Serguei Maliar, 2009. "Solving the incomplete markets model with aggregate uncertainty using the Krusell-Smith algorithm," Working Papers. Serie AD 2009-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  11. Michael Creel, 2008. "Using Parallelization to Solve a Macroeconomic Model: A Parallel Parameterized Expectations Algorithm," Computational Economics, Society for Computational Economics, vol. 32(4), pages 343-352, November.

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