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Master's Dissertation
Full name
Pedro Luiz Paulino Chaim
Knowledge Area
Date of Defense
Ribeirão Preto, 2016
Laurini, Marcio Poletti (President)
Araujo Junior, Eurilton Alves
Caldeira, João Frois
Gomes, Fabio Augusto Reis
Title in English
Estimation and Identification of a DSGE model: an Application of the Data Cloning Methodology
Keywords in English
Bayesian Asymptotics
Data Cloning
Maximum Likelihood
Abstract in English
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.
Title in Portuguese
Estimação e identificação de um Modelo DSGE: uma applicação da metodologia data cloning
Keywords in Portuguese
Data Cloning
Estatística Bayesiana
Máxima Verossimilhança
Abstract in Portuguese
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.
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