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Parameterized Expectations Algorithm: How To Solve For Labor Easily

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  • Lilia Maliar

    ()
    (Universidad de Alicante)

  • Serguei Maliar

    (Universidad de Alicante)

Abstract

Euler-equation methods for solving nonlinear dynamic models involve parameterizing some policy functions. We argue that in the typical macroeconomic model with valuable leisure, labor function is particularly convenient for parameterizing. This is because under the labor-function parameterization, the intratemporal first-order condition admits a closed-form solution, while under other parameterizations, there should be a numerical solution. In the context of a simulation-based parameterized expectations algorithm, we find that using the labor-function parameterization instead of the standard consumption-function parameterization reduces computational time by more than a factor of ten.

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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 2004-40.

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Length: 14 pages
Date of creation: Oct 2004
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2004-40

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Keywords: Nonlinear models; Parameterized expectations; PEA; Monte Carlo simulation; Numerical solution;

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References

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  1. 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.
  2. Lawrence J. Christiano & Jonas D. M. Fisher, 1994. "Algorithms for solving dynamic models with occasionally binding constraints," Staff Report 171, Federal Reserve Bank of Minneapolis.
  3. Maliar, Lilia & Maliar, Serguei, 2001. "Heterogeneity in capital and skills in a neoclassical stochastic growth model," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1367-1397, September.
  4. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, January.
  5. Coleman, Wilbur John, II, 1990. "Solving the Stochastic Growth Model by Policy-Function Iteration," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 27-29, January.
  6. 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.
  7. Albert Marcet & Guido Lorenzoni, 1998. "The Parameterized Expectations Approach: Some Practical Issues," QM&RBC Codes 128, Quantitative Macroeconomics & Real Business Cycles.
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Citations

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Cited by:
  1. Lilia Maliar & Kateryna Garmel & Serguei Maliar, 2005. "The Eu Eastern Enlargement And Fdi: The Implications From A Neoclassical Growth Model," Working Papers. Serie AD 2005-29, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  2. Hull, Isaiah, 2013. "Approximate dynamic programming with postdecision states as a solution method for dynamic economic models," Working Paper Series 276, Sveriges Riksbank (Central Bank of Sweden).
  3. Pérez, Javier J. & Sánchez, A. Jesús, 2009. "Alternatives to initialize the Parameterized Expectations Algorithm," Economics Letters, Elsevier, vol. 102(2), pages 116-118, February.

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