 Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copulaCristiano Villa and Francisco J. Rubio Computational Statistics & Data Analysis, 2018, vol. 124, issue C, 197-219 Abstract: An objective Bayesian approach to estimate the number of degrees of freedom (ν) for the multivariate t distribution and for the t-copula, when the parameter is considered discrete, is proposed. Inference on this parameter has been problematic for the multivariate t and, for the absence of any method, for the t-copula. An objective criterion based on loss functions which allows to overcome the issue of defining objective probabilities directly is employed. The support of the prior for ν is truncated, which derives from the property of both the multivariate t and the t-copula of convergence to normality for a sufficiently large number of degrees of freedom. The performance of the priors is tested on simulated scenarios 11The R codes and the replication material are available as a supplementary material of the electronic version of the paper.and on real data: daily logarithmic returns of IBM and of the Center for Research in Security Prices Database. Keywords: Information loss; Kullback–Leibler divergence; Log-returns; Multivariate t distribution; Objective prior; t-copula (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:124:y:2018:i:c:p:197-219 DOI: 10.1016/j.csda.2018.03.010 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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