Higher order approximations of stochastic rational expectations models
AbstractWe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 3913.
Date of creation: Jul 2007
Date of revision:
perturbation method; DSGE models;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-07-13 (All new papers)
- NEP-CBA-2007-07-13 (Central Banking)
- NEP-CMP-2007-07-13 (Computational Economics)
- NEP-DGE-2007-07-13 (Dynamic General Equilibrium)
- NEP-ECM-2007-07-13 (Econometrics)
- NEP-MAC-2007-07-13 (Macroeconomics)
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- Kowal, Pawel, 2006. "A note on differentiating matrices," MPRA Paper 1239, University Library of Munich, Germany.
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