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

Publikation: Working/Discussion PaperWU Working Paper

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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)
HerausgeberDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
PublikationsstatusVeröffentlicht - 1993


NameForschungsberichte / Institut für Statistik

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