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Higher order approximations of stochastic rational expectations models

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  • Kowal, Pawel

Abstract

We describe algorithm to find higher order approximations of stochastic rational expectations models near the deterministic steady state. Using matrix representation of function derivatives instead of tensor representation we obtain simple expressions of matrix equations determining higher order terms.

Suggested Citation

  • Kowal, Pawel, 2007. "Higher order approximations of stochastic rational expectations models," MPRA Paper 3913, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:3913
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    File URL: https://mpra.ub.uni-muenchen.de/3913/1/MPRA_paper_3913.pdf
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    References listed on IDEAS

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    1. Kowal, Pawel, 2006. "A note on differentiating matrices," MPRA Paper 1239, University Library of Munich, Germany.
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    Cited by:

    1. repec:hal:spmain:info:hdl:2441/3ug0u3qte39q7rqvbmij9rb993 is not listed on IDEAS
    2. Jean Barthélemy & Magali Marx, 2012. "Solving Rational Expectations Models," Sciences Po publications info:hdl:2441/3ug0u3qte39, Sciences Po.

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      Keywords

      perturbation method; DSGE models;

      JEL classification:

      • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
      • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
      • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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