Fully Bayesian Analysis of Switching Gaussian State Space Models View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-03

AUTHORS

Sylvia Frühwirth-Schnatter

ABSTRACT

In the present paper we study switching state space models from a Bayesian point of view. We discuss various MCMC methods for Bayesian estimation, among them unconstrained Gibbs sampling, constrained sampling and permutation sampling. We address in detail the problem of unidentifiability, and discuss potential information available from an unidentified model. Furthermore the paper discusses issues in model selection such as selecting the number of states or testing for the presence of Markov switching heterogeneity. The model likelihoods of all possible hypotheses are estimated by using the method of bridge sampling. We conclude the paper with applications to simulated data as well as to modelling the U.S./U.K. real exchange rate. More... »

PAGES

31-49

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1017908219076

DOI

http://dx.doi.org/10.1023/a:1017908219076

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014909694


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