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metadata.artigo.dc.title: Bayesian analysis of dynamic factor models using multivariate T distribution
metadata.artigo.dc.creator: Andrade, Larissa Ribeiro de
Ferreira, Daniel Furtado
Sáfadi, Thelma
Barroso, Lúcia Pereira
metadata.artigo.dc.subject: Factor models
Gibbs samples
Multivariate t
Modelos de fator
Amostras de Gibbs
T multivariada
metadata.artigo.dc.publisher: Universidade Federal de Lavras 2018
metadata.artigo.dc.identifier.citation: ANDRADE, L. R. de et al. Bayesian analysis of dynamic factor models using multivariate T distribution. Revista Brasileira de Biometria, Lavras, v. 36, n. 1, p. 140-156, mar. 2018.
metadata.artigo.dc.description.abstract: The multivariate t models are symmetric and have heavier tail than the normal distribution and produce robust inference procedures for applications. In this paper, the Bayesian estimation of a dynamic factor model is presented, where the factors follow a multivariate autoregressive model, using the multivariate t distribution. Since the multivariate t distribution is complex, it was represented in this work as a mix of the multivariate normal distribution and a square root of a chi-square distribution. This method allowed the complete dene of all the posterior distributions. The inference on the parameters was made taking a sample of the posterior distribution through a Gibbs Sampler. The convergence was veried through graphical analysis and the convergence diagnostics of Geweke (1992) and Raftery and Lewis (1992).
metadata.artigo.dc.language: en_US
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