Bayesian Model Discrimination and Bayes Factors for Normal Linear State Space Models

Publication: Working/Discussion PaperWU Working Paper

Abstract

It is suggested to discriminate between different state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. Practical implementation of this procedures requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters which is carried out by Markov chain Monte Carlo methods. Application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
Publication statusPublished - 1993

Publication series

NameForschungsberichte / Institut für Statistik
No.33

WU Working Paper Series

  • Forschungsberichte / Institut für Statistik

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