Creator: Geweke, John and Petrella, Lea Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 553 Abstract:
This paper provides a general and efficient method for computing density ratio class bounds on posterior moments, given the output of a posterior simulator. It shows how density ratio class bounds for posterior odds ratios may be formed in many situations, also on the basis of posterior simulator output. The computational method is used to provide density ratio class bounds in two econometric models. It is found that the exact bounds are approximated poorly by their asymptotic approximation, when the posterior distribution of the function of interest is skewed. It is also found that posterior odds ratios display substantial variation within the density ratio class, in ways that cannot be anticipated by the asymptotic approximation.
关键词: Bayesian inference, Markov-chain Monte Carlo, Normal mixture, and Probit model 学科: C11 - Bayesian Analysis: General and C63 - Computational Techniques; Simulation Modeling