Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology

• Ano de defesa: 2016
• Autor(a) principal: Chaim, Pedro Luiz Paulino
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Bayesian Asymptotics; Data Cloning; DSGE; Estatística Bayesiana; Estimação; Estimation; Identificação; Identification; Máxima Verossimilhança; Maximum Likelihood
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/96/96131/tde-31032016-144306/

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.