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Stochastic Simulations of a Non-Linear Phillips Curve Model

Author

Listed:
  • Michel Juillard

    (University Paris VIII and CEPREMAP)

  • Fabrice Collard

    (CEPREMAP)

Abstract

This paper presents stochastic simulations of a non-linear Phillips curve model with a random shock on the labor market, a random shock on inflation, and 20 state variables to represent a rather complex dynamical adjustment. Various methods are used to perform the simulations: two approaches to parameterized-expectations and a high-order Taylor expansion. The effects of non-linearity are then evaluated by a comparison with a linearized version of the model.

Suggested Citation

  • Michel Juillard & Fabrice Collard, 1999. "Stochastic Simulations of a Non-Linear Phillips Curve Model," Computing in Economics and Finance 1999 144, Society for Computational Economics.
    • Handle: RePEc:sce:scecf9:144
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    File URL: http://www.cepremap.cnrs.fr/~michel/feb99/coljui.pdf
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    References listed on IDEAS

    as
    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.
    2. McGrattan, Ellen R., 1996. "Solving the stochastic growth model with a finite element method," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 19-42.
    3. 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.
    4. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
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