Bayesian analysis of reduced rank regression models using post-processing
Loading...
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Bayesian estimation of reduced rank regression models requires careful consideration of the
well known identification problem. We demonstrate that this identification problem can be handled
efficiently by using prior distributions that restrict a part of the parameter space to the
Stiefel manifold and post-processing the obtained Gibbs sampler output according to an appropriately
specified loss function. This extends the possibilities for Bayesian inference in reduced
rank regression models. Besides inference, we also discuss model selection in terms of posterior
predictive assessment. We choose this approach because computing the marginal data likelihood
under the identifying restrictions implies prohibitive computational burden. We illustrate the
proposed approach with a simulation study and an empirical application.
Description
Table of contents
Keywords
Bayesian estimation, posterior predictive assessment, Stiefel manifold, model selection, orthogonal transformation, reduced rank regression