Bayesian inference for factor structure models via gibbs sampling

Abstract

"The goal of this paper is to provide all the technical details required to implement Gibbs sampling for the estimation of simultaneous equation models with common latent factors among their regressors, so-called "factor structure models". Linear, dichotomous and censored response models, as well as ordered and unordered response models can be accommodated in this framework. The latent factors can be either correlated or not, and specified as normally distributed or as following a finite mixture of normal distributions for more flexibility. All conditional distributions are derived and can be used to construct the Gibbs sampler step by step." (author's abstract)