1 research outputs found
Simulation based bayesian econometric inference: principles and some recent computational advances.
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