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Bayesian estimation of a multivariate TAR model when the noise process follows a Student-t distribution

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  • Lizet Viviana Romero Orjuela
  • Sergio Alejandro Calderón Villanueva

Abstract

In this paper, we introduce a Bayesian methodology for the estimation of non-structural parameters (autoregressive matrices, covariance matrices and degrees of freedom) of a multivariate TAR model (MTAR) when noise process follows a multivariate Student-t distribution. For this, the use of non-informative prior distributions is proposed to obtain the full conditional distributions. MCMC methods are used to obtain samples of such distributions. The performance of the estimation is evaluated by means simulations. Finally, the model is applied to the returns data from the Bovespa, Colcap and Standard and Poor indexes.

Suggested Citation

  • Lizet Viviana Romero Orjuela & Sergio Alejandro Calderón Villanueva, 2021. "Bayesian estimation of a multivariate TAR model when the noise process follows a Student-t distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(11), pages 2508-2530, June.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:11:p:2508-2530
    DOI: 10.1080/03610926.2019.1669807
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