Simulation based Bayesian econometric inference: principles and some recent computational advances

Lennart F. Hoogerheide, Herman van Dijk and Rutger D. van Oest

No 2007015, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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 MetropolisHastings 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.

JEL-codes: C11 C12 C15 C45 C52 (search for similar items in EconPapers)
Date: 2007-03
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Citations: View citations in EconPapers (4)

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Working Paper: Simulation based bayesian econometric inference: principles and some recent computational advances (2007)
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