Seemingly unrelated regression equations models
Seemingly unrelated regression equations models
Computational Statistics & Data Analysis
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
An Introduction to Variational Methods for Graphical Models
Machine Learning
Sparse graphical models for exploring gene expression data
Journal of Multivariate Analysis
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Seemingly unrelated regression model with unequal size observations: computational aspects
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
A sparse seemingly unrelated regression (SSUR) model is proposed to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated regression (SUR) model parameters. The SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR model to dynamic models embed general structure constraints and model uncertainty in dynamic models. The models represent specific varieties of models recently developed in the growing high-dimensional sparse modelling literature. Two simulated examples illustrate the model and highlight questions regarding model uncertainty, searching, and comparison. The model is then applied to two real-world examples in macroeconomics and finance, according to which its identified structures have practical significance.