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Stochastic Simulations of a Non-Linear Phillips Curve Model

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  • 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.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 144.

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Date of creation: 01 Mar 1999
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Handle: RePEc:sce:scecf9:144

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  1. Lawrence J. Christiano & Jonas D.M. Fisher, 1997. "Algorithms for solving dynamic models with occasionally binding constraints," Working Paper Series, Macroeconomic Issues WP-97-15, Federal Reserve Bank of Chicago.
  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. Ellen R. McGrattan, 1993. "Solving the stochastic growth model with a finite element method," Staff Report 164, Federal Reserve Bank of Minneapolis.
  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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