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    An alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models

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    DynamicSBeta_Aug02pm_2012.pdf (453.9Kb)
    Date
    2014
    Author
    Fúquene, Jairo
    Pérez, María Eglée
    Pericchi Guerra, Luis Raúl
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    Abstract
    In this paper, we propose a new wide class of hypergeometric heavy tailed priors that is given as the convolution of a Student-t density for the location parameter and a Scaled Beta 2 prior for the squared scale parameter. These priors may have heavier tails than Student-t priors, and the variances have a sensible behaviour both at the origin and at the tail, making it suitable for objective analysis. Since the representation of our proposal is a scale mixture, it is suitable to detect sudden changes in the model. Finally, we propose a Gibbs sampler using this new family of priors for modelling outliers and structural breaks in Bayesian dynamic linear models. We demonstrate in a published example, that our proposal is more suitable than the Inverted Gamma’s assumption for the variances, which makes very hard to detect structural changes.
    URI
    http://hdl.handle.net/123456789/2406
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