Bayesian model comparison by Markov chain simulation: Illustration using stock market data
نویسنده
چکیده
The paper illustrates the computation of marginal likelihoods and Bayes factors when Markov Chain Monte Carlo has been used to produce draws from a model’s posterior distribution. The method is based on Raftery (1996) and does not require that Gibbs sampling is used or conditional posterior distributions are available in closed form. Models used include a normal finite mixture, a GARCH and a Student t-model as alternative models for the Standard and Poor’s stock returns. 2000 University of Venice
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