Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
We provide a summarized presentation of solution methods for rational expectations models, based on eigenvalue/eigenvector decompositions. These methods solve systems of stochastic linear difference equations by relying on the use of stability conditions derived from the eigenvectors associated to u...
| Autores: | , , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 1998 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/64209 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/64209 |
| Access Level: | acceso abierto |
| Palabra clave: | Eigenvalue-eigenvector decompositions Numerical solutions Rational expectations. Procesos estocásticos 1208.08 Procesos Estocásticos |
| Sumario: | We provide a summarized presentation of solution methods for rational expectations models, based on eigenvalue/eigenvector decompositions. These methods solve systems of stochastic linear difference equations by relying on the use of stability conditions derived from the eigenvectors associated to unstable eigenvalues of the coefficient matrices in the system. For nonlinear models, a linear approximation must be obtained, and the stability conditions are approximate, This is however, the only source of approximation error, since the nonlinear structure of the original model is used to produce the numerical solution. After applying the method to a baseline stochastic growth model, we explain how it can be used: i) to salve some identification problems that may arise in standard growth models, and ii) to solve endogenous growth models. |
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