Template-Type: ReDIF-Paper 1.0
Author-Name: Hoogerheide, L.F.
Author-Name-Last: Hoogerheide
Author-Name-First: Lennart
Author-Name: van Dijk, H.K.
Author-Name-Last: van Dijk
Author-Name-First: Herman
Author-Person: pva325
Author-Name: van Oest, R.D.
Author-Name-Last: van Oest
Author-Name-First: Rutger
Title: Simulation based bayesian econometric inference: principles and some recent computational advances.
Abstract: 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.
Creation-Date: 2007-01-31
File-URL: https://repub.eur.nl/pub/8523/EI200703.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2007-03
Handle: RePEc:ems:eureir:8523