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...

ver descrição completa

Detalhes bibliográficos
Autores: Novales Cinca, Alfonso Santiago, Domínguez Irastorza, Emilio, Pérez, Javier, Ruiz Andújar, Jesús
Formato: informe técnico
Fecha de publicación:1998
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/64209
Acesso em linha:https://hdl.handle.net/20.500.14352/64209
Access Level:acceso abierto
Palavra-chave:Eigenvalue-eigenvector decompositions
Numerical solutions
Rational expectations.
Procesos estocásticos
1208.08 Procesos Estocásticos
id ES_7942e045d1b0b40a55854a8263adbf03
oai_identifier_str oai:docta.ucm.es:20.500.14352/64209
network_acronym_str ES
network_name_str España
repository_id_str
spelling Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositionsNovales Cinca, Alfonso SantiagoDomínguez Irastorza, EmilioPérez, JavierRuiz Andújar, JesúsEigenvalue-eigenvector decompositionsNumerical solutionsRational expectations.Procesos estocásticos1208.08 Procesos EstocásticosWe 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.Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)Universidad Complutense de Madrid19981998-01-0119981998-01-01technical reporthttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14352/64209reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-CompartirIgual 3.0 Españahttps://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/642092026-06-02T12:44:21Z
dc.title.none.fl_str_mv Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
title Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
spellingShingle Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
Novales Cinca, Alfonso Santiago
Eigenvalue-eigenvector decompositions
Numerical solutions
Rational expectations.
Procesos estocásticos
1208.08 Procesos Estocásticos
title_short Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
title_full Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
title_fullStr Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
title_full_unstemmed Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
title_sort Solving nonlinear rational expectations models by eigenvalue-eigenvector decompositions
dc.creator.none.fl_str_mv Novales Cinca, Alfonso Santiago
Domínguez Irastorza, Emilio
Pérez, Javier
Ruiz Andújar, Jesús
author Novales Cinca, Alfonso Santiago
author_facet Novales Cinca, Alfonso Santiago
Domínguez Irastorza, Emilio
Pérez, Javier
Ruiz Andújar, Jesús
author_role author
author2 Domínguez Irastorza, Emilio
Pérez, Javier
Ruiz Andújar, Jesús
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Eigenvalue-eigenvector decompositions
Numerical solutions
Rational expectations.
Procesos estocásticos
1208.08 Procesos Estocásticos
topic Eigenvalue-eigenvector decompositions
Numerical solutions
Rational expectations.
Procesos estocásticos
1208.08 Procesos Estocásticos
description 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.
publishDate 1998
dc.date.none.fl_str_mv 1998
1998-01-01
1998
1998-01-01
dc.type.none.fl_str_mv technical report
http://purl.org/coar/resource_type/c_18gh
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/64209
url https://hdl.handle.net/20.500.14352/64209
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-CompartirIgual 3.0 España
https://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-CompartirIgual 3.0 España
https://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
publisher.none.fl_str_mv Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411334981943296
score 15,228081

Registos relacionados