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Title: Bayesian analysis of dynamic factor models using multivariate T distribution
Keywords: Factor models
Gibbs sampler
Multivariate t.
Modelos de fator
Amostragem Gibbs
Multivariada t.
Issue Date: 2018
Publisher: Universidade Federal de Lavras
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.
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).
Appears in Collections:DES - Artigos publicados em periódicos

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