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Is it Worth Refining Linear Approximations to Non-Linear Rational Expectations Models?

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Abstract

We characterize the balanced growth path of the basic neoclassical growth economy using standard, almost linear numerical solution methods, as well as the parameterized expectations approach, which preserves the nonlinearity in the model. We also apply the same methods after adding indivisible labor to the basic model, and to a monetary version of that economy, subject to a cash-in-advance constraint. In a unified framework we tackle the question of how much of the nonlinear structure of the original problem is useful to maintain when using an “almost” linear method. We show that it is possible to find an almost linear method to solve these models as accurately as by parameterizing expectations. Our results show the importance of performing log-linear approximations, as well as the convenience of refining a linear solution method by mixing some structure of the original non-linear problem with structure of the approximated system.

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  • Alfonso Novales & Javier J. Pérez, 2002. "Is it Worth Refining Linear Approximations to Non-Linear Rational Expectations Models?," Economic Working Papers at Centro de Estudios Andaluces E2002/15, Centro de Estudios Andaluces.
  • Handle: RePEc:cea:doctra:e2002_15
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    Cited by:

    1. Alfonso Novales & Javier J. PÈrez, 2004. "Is It Worth Refining Linear Approximations to Non-Linear Rational Expectations Models?," Computational Economics, Springer;Society for Computational Economics, vol. 23(4), pages 343-377, June.
    2. Antonio Morales & Pablo Brañas Garza, 2003. "Computational Errors in Guessing Games1," Economic Working Papers at Centro de Estudios Andaluces E2003/11, Centro de Estudios Andaluces.
    3. Paul Pichler, 2005. "Evaluating Approximate Equilibria of Dynamic Economic Models," Vienna Economics Papers 0510, University of Vienna, Department of Economics.

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    More about this item

    Keywords

    Linear-quadratic approximation; numerical accuracy; simulation; numerical methods.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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