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Title: A topological view on the identification of structural vector autoregressions Authors:  Klaus Neusser - University of Bern (Switzerland) [presenting]
Abstract: The notion of the group of orthogonal matrices acting on the set of all feasible identification schemes is used to characterize the identification problem arising in structural vector autoregressions. This approach presents several conceptual advantages. First, it provides a justification for the use of the normalized Haar measure as the natural uninformative prior. This specification of priors corresponds to the invariance principle. Second, using the multivariate beta distribution we derive the joint distribution of blocks of parameters defining an identification scheme. Finally, it allows the coherent introduction of perturbed identification schemes which is of relevance for the specification of time-varying vector autoregressions.