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Stochastic Simulations of a Non-Linear Phillips Curve Model

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Author Info
Michel Juillard () (University Paris VIII and CEPREMAP)
Fabrice Collard () (CEPREMAP)

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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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File URL: http://www.cepremap.cnrs.fr/~michel/feb99/coljui.pdf
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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. 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.
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  2. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December. [Downloadable!] (restricted)
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  3. Lawrence J. Christiano & Jonas D.M. Fisher, 1994. "Algorithms for solving dynamic models with occasionally binding constraints," Working Paper Series, Macroeconomic Issues 94-6, Federal Reserve Bank of Chicago.
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  4. Ellen R. McGrattan, 1993. "Solving the stochastic growth model with a finite element method," Staff Report 164, Federal Reserve Bank of Minneapolis. [Downloadable!]
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