Novales Cinca, Alfonso SantiagoDomínguez, EmilioPérez, JavierRuiz, Jesús2023-06-212023-06-211998https://hdl.handle.net/20.500.14352/64209We 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.engAtribución-NoComercial-CompartirIgual 3.0 Españahttps://creativecommons.org/licenses/by-nc-sa/3.0/es/technical reporthttp://www.ucm.es/icaeopen accessEigenvalue-eigenvector decompositionsNumerical solutionsRational expectations.Procesos estocásticos1208.08 Procesos Estocásticos