TY - JOUR
AU - ANDRADE, Larissa Ribeiro de
AU - FERREIRA, Daniel Furtado
AU - SÁFADI, Thelma
AU - BARROSO, Lúcia Pereira
PY - 2018/03/28
Y2 - 2024/06/17
TI - BAYESIAN ANALYSIS OF DYNAMIC FACTOR MODELS USING MULTIVARIATE T DISTRIBUTION
JF - Brazilian Journal of Biometrics
JA - Braz. J. Biom.
VL - 36
IS - 1
SE - Articles
DO - 10.28951/rbb.v36i1.155
UR - http://www.biometria.ufla.br/index.php/BBJ/article/view/155
SP - 140-156
AB - <p>The multivariate<em> t</em> 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 <em>t</em> 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).</p>
ER -