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Parameterized Expectations Algorithm: How to Solve for Labor Easily

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Author Info
Lilia Maliar
Serguei Maliar ()

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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 10. Copyright Springer Science + Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s10614-005-2224-9
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Publisher Info
Article provided by Springer in its journal Computational Economics.

Volume (Year): 25 (2005)
Issue (Month): 3 (June)
Pages: 269-274
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Handle: RePEc:kap:compec:v:25:y:2005:i:3:p:269-274

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Related research
Keywords: Monte Carlo simulation; nonlinear models; numerical solution; parameterized expectations; PEA;

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  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. [Downloadable!] (restricted)
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  2. Albert Marcet & Guido Lorenzoni, 1998. "The Parameterized Expectations Approach: Some Practical Issues," QM&RBC Codes 128, Quantitative Macroeconomics & Real Business Cycles. [Downloadable!]
  3. 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.
  4. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March. [Downloadable!]
  5. 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.
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  6. Dr. Peter Kenning & Hilke Plassmann, 2004. "NeuroEconomics," Experimental 0412005, EconWPA. [Downloadable!]
  7. 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. [Downloadable!] (restricted)
  8. 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. [Downloadable!] (restricted)
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  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). [Downloadable!]
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