High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models.

ABSTRACT: Vector autoregressive (VAR) models aim to capture linear temporal interdependencies amongst multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for regularized estimates of the model's parameters. However, hardly anything is known regarding properties of the posterior distribution for Bayesian VAR models in such regimes. In this work, we consider a VAR model with two prior choices for the autoregressive coefficient matrix: a non-hierarchical matrix-normal prior and a hiera

SUBMITTER: Ghosh S 

PROVIDER: S-EPMC6716151 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature