期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2007
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：In this paper we discuss several aspects of simulation based Bayesian econometric
inference. We start at an elementary level on basic concepts of Bayesian analysis;
evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference.
Next, the most popular and well-known simulation techniques are discussed, the
Metropolis-Hastings algorithm and Gibbs sampling (being the most popular Markov chain
Monte Carlo methods) and importance sampling. After that, we discuss two recently
developed sampling methods: adaptive radial based direction sampling [ARDS], which
makes use of a transformation to radial coordinates, and neural network sampling, which
makes use of a neural network approximation to the posterior distribution of interest. Both
methods are especially useful in cases where the posterior distribution is not well-behaved,
in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated
in several example models, such as a model for the real US GNP and models for binary data
of a US recession indicator.
