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

Frühwirth-Schnatter, Sylvia (1993) Bayesian Model Discrimination and Bayes Factors for Normal Linear State Space Models. Forschungsberichte / Institut für Statistik, 33. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

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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)

Item Type: Paper
Keywords: Bayes factors / Markov chain Monte Carlo / model discrimination / model likelihood / state space models / training sample priors
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 11 Jul 2006 09:28
Last Modified: 16 Jun 2019 03:39
URI: https://bach-s59.wu.ac.at/id/eprint/108

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