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Parameterized Expectations Algorithm: How To Solve For Labor Easily

Author

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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.

Suggested Citation

  • Lilia Maliar & Serguei Maliar, 2004. "Parameterized Expectations Algorithm: How To Solve For Labor Easily," Working Papers. Serie AD 2004-40, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  • Handle: RePEc:ivi:wpasad:2004-40
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Albert Marcet & Guido Lorenzoni, 1998. "The Parameterized Expectations Approach: Some Practical Issues," QM&RBC Codes 128, Quantitative Macroeconomics & Real Business Cycles.
    7. 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.
    8. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March.
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    Citations

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    Cited by:

    1. 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.
    2. 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, vol. 35(2), pages 207-228, February.
    3. 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).
    4. Garmel, Kateryna & Maliar, Lilia & Maliar, Serguei, 2008. "EU eastern enlargement and foreign investment: Implications from a neoclassical growth model," Journal of Comparative Economics, Elsevier, vol. 36(2), pages 307-325, June.
    5. Pérez, Javier J. & Sánchez, A. Jesús, 2009. "Alternatives to initialize the Parameterized Expectations Algorithm," Economics Letters, Elsevier, vol. 102(2), pages 116-118, February.
    6. 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.

    More about this item

    Keywords

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

    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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