Template-Type: ReDIF-Paper 1.0
Author-Name: Hoogerheide, L.F.
Author-Name-Last: Hoogerheide
Author-Name-First: Lennart
Author-Name: Kaashoek, J.F.
Author-Name-Last: Kaashoek
Author-Name-First: Johan
Author-Name: van Dijk, H.K.
Author-Name-Last: van Dijk
Author-Name-First: Herman
Author-Person: pva325
Title: On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks
Abstract: Likelihoods and posteriors of instrumental variable regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating such contours using Monte Carlo integration methods like importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm and the quality of the results greatly depend on the choice of the importance or candidate density. Such a density has to be `close' to the target density in order to yield accurate results with numerically efficient sampling. For this purpose we  introduce neural networks which seem to be natural importance or candidate densities,  as they have a universal approximation property and are easy to sample from. A key step in the proposed class of methods is the construction of a neural network  that approximates the target density accurately. The methods are tested on a set of illustrative models. The results indicate the feasibility of the neural network approach.
Creation-Date: 2005-03-31
File-URL: https://repub.eur.nl/pub/2007/ei200512.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2005-12
Classification-JEL: C11, C15, C45
Keywords: Bayesian inference, Markov chain Monte Carlo, credible sets, importance sampling, instrumental variables, neural networks, reduced rank
Handle: RePEc:ems:eureir:2007