BAYESIAN ANALYSIS OF DYNAMIC FACTOR MODELS USING MULTIVARIATE T DISTRIBUTION

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Larissa Ribeiro de ANDRADE
Daniel Furtado FERREIRA
Thelma SÁFADI
Lúcia Pereira BARROSO

Resumo

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).

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Como Citar
ANDRADE, L. R. de, FERREIRA, D. F., SÁFADI, T., & BARROSO, L. P. (2018). BAYESIAN ANALYSIS OF DYNAMIC FACTOR MODELS USING MULTIVARIATE T DISTRIBUTION. REVISTA BRASILEIRA DE BIOMETRIA, 36(1), 140–156. https://doi.org/10.28951/rbb.v36i1.155
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