Fully Bayesian Analysis of Switching Gaussian State Space Models

Publikation: Working/Discussion PaperWU Working Paper

Abstract

In the present paper we study switching state space models from a Bayesian point of view. For estimation, the model is reformulated as a hierarchical model. 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. (author's abstract)
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
PublikationsstatusVeröffentlicht - 2000

Publikationsreihe

NameForschungsberichte / Institut für Statistik
Nr.73

WU Working Paper Reihe

  • Forschungsberichte / Institut für Statistik

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