Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244563 
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 5/2020
Publisher: 
Örebro University School of Business, Örebro
Abstract: 
We propose a general class of multivariate fat-tailed distributions which includes the normal, t and Laplace distributions as special cases as well as their mixture. Full conditional posterior distributions for the Bayesian VAR-model are derived and used to construct a MCMC-sampler for the joint posterior distribution. The framework allows for selection of a specific special case as the distribution for the error terms in the VAR if the evidence in the data is strong while at the same time allowing for considerable flexibility and more general distributions than offered by any of the special cases. As fat tails can also be a sign of conditional heteroskedasticity we also extend the model to allow for stochastic volatility. The performance is evaluated using simulated data and the utility of the general model specification is demonstrated in applications to macroeconomics.
Subjects: 
Scale mixture of normals
Elliptically contoured distribution
Mixture distributions
Stochastic volatility
Markov Chain Monte Carlo
JEL: 
C11
C15
C16
C32
C52
Document Type: 
Working Paper

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