Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology
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Publication Date: | 2016 |
Format: | Master thesis |
Language: | eng |
Source: | Biblioteca Digital de Teses e Dissertações da USP |
Download full: | https://doi.org/10.11606/D.96.2016.tde-31032016-144306 |
Summary: | We apply the data cloning method developed by Lele et al. (2007) to estimate the model of Smets and Wouters (2007). The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators. We also analyze the identification properties of the model. We measure the individual identification strength of each parameter by observing the posterior volatility of data cloning estimates, and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix. Our results indicate that the model is only poorly identified. The system displays bad global identification properties, and most of its parameters seem locally ill-identified. |
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info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology Estimação e identificação de um Modelo DSGE: uma applicação da metodologia data cloning 2016-01-18Marcio Poletti LauriniEurilton Alves Araujo JuniorJoão Frois CaldeiraFabio Augusto Reis GomesPedro Luiz Paulino ChaimUniversidade de São PauloEconomiaUSPBR Bayesian Asymptotics Data Cloning Data Cloning DSGE DSGE Estatística Bayesiana Estimação Estimation Identificação Identification Máxima Verossimilhança Maximum Likelihood We apply the data cloning method developed by Lele et al. (2007) to estimate the model of Smets and Wouters (2007). The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators. We also analyze the identification properties of the model. We measure the individual identification strength of each parameter by observing the posterior volatility of data cloning estimates, and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix. Our results indicate that the model is only poorly identified. The system displays bad global identification properties, and most of its parameters seem locally ill-identified. Neste trabalho aplicamos o método data cloning de Lele et al. (2007) para estimar o modelo de Smets e Wouters (2007). O algoritmo data cloning é um método numérico que utiliza réplicas da amostra original para aproximar o estimador de máxima verossimilhança como limite de estimadores Bayesianos obtidos por simulação. Nós também analisamos a identificação dos parâmetros do modelo. Medimos a identificação de cada parâmetro individualmente ao observar a volatilidade a posteriori dos estimadores de data cloning. O maior autovalor da matriz de covariância a posteriori proporciona uma medida global de identificação do modelo. Nossos resultados indicam que o modelo de Smets e Wouters (2007) não é bem identificado. O modelo não apresenta boas propriedades globais de identificação, e muitos de seus parâmetros são localmente mal identificados. https://doi.org/10.11606/D.96.2016.tde-31032016-144306info:eu-repo/semantics/openAccessengreponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USP2023-12-21T20:28:37Zoai:teses.usp.br:tde-31032016-144306Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212023-12-22T13:33:58.624259Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.en.fl_str_mv |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
dc.title.alternative.pt.fl_str_mv |
Estimação e identificação de um Modelo DSGE: uma applicação da metodologia data cloning |
title |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
spellingShingle |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology Pedro Luiz Paulino Chaim |
title_short |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
title_full |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
title_fullStr |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
title_full_unstemmed |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
title_sort |
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology |
author |
Pedro Luiz Paulino Chaim |
author_facet |
Pedro Luiz Paulino Chaim |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Marcio Poletti Laurini |
dc.contributor.referee1.fl_str_mv |
Eurilton Alves Araujo Junior |
dc.contributor.referee2.fl_str_mv |
João Frois Caldeira |
dc.contributor.referee3.fl_str_mv |
Fabio Augusto Reis Gomes |
dc.contributor.author.fl_str_mv |
Pedro Luiz Paulino Chaim |
contributor_str_mv |
Marcio Poletti Laurini Eurilton Alves Araujo Junior João Frois Caldeira Fabio Augusto Reis Gomes |
description |
We apply the data cloning method developed by Lele et al. (2007) to estimate the model of Smets and Wouters (2007). The data cloning algorithm is a numerical method that employs replicas of the original sample to approximate the maximum likelihood estimator as the limit of Bayesian simulation-based estimators. We also analyze the identification properties of the model. We measure the individual identification strength of each parameter by observing the posterior volatility of data cloning estimates, and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix. Our results indicate that the model is only poorly identified. The system displays bad global identification properties, and most of its parameters seem locally ill-identified. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016-01-18 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.11606/D.96.2016.tde-31032016-144306 |
url |
https://doi.org/10.11606/D.96.2016.tde-31032016-144306 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo |
dc.publisher.program.fl_str_mv |
Economia |
dc.publisher.initials.fl_str_mv |
USP |
dc.publisher.country.fl_str_mv |
BR |
publisher.none.fl_str_mv |
Universidade de São Paulo |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
_version_ |
1794503171879469056 |