Bayesian analysis of dynamic factor models using multivariate T distribution

dc.creatorAndrade, Larissa Ribeiro de
dc.creatorFerreira, Daniel Furtado
dc.creatorSáfadi, Thelma
dc.creatorBarroso, Lúcia Pereira
dc.date2019-05-07T13:06:33Z
dc.date2019-05-07T13:06:33Z
dc.date2018
dc.date.accessioned2023-09-28T19:56:21Z
dc.date.available2023-09-28T19:56:21Z
dc.identifierANDRADE, 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.
dc.identifierhttp://www.biometria.ufla.br/index.php/BBJ/article/view/155
dc.identifierhttp://repositorio.ufla.br/jspui/handle/1/34021
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9040642
dc.descriptionThe 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).
dc.languageen_US
dc.publisherUniversidade Federal de Lavras
dc.rightsrestrictAccess
dc.sourceRevista Brasileira de Biometria
dc.subjectFactor models
dc.subjectGibbs samples
dc.subjectMultivariate t
dc.subjectModelos de fator
dc.subjectAmostras de Gibbs
dc.subjectT multivariada
dc.titleBayesian analysis of dynamic factor models using multivariate T distribution
dc.typeArtigo


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