High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Issue 526 (3rd April 2019)

• Record Type: Journal Article
• Authors: Ghosh, Satyajit; Khare, Kshitij; Michailidis, George
• Is Part Of: Journal of the American Statistical Association. Volume 114:Issue 526(2019)
• Page Start: 735
• Page End: 748
• Publication Date: 2019-04-03
• Subjects: Bayesian lasso -- Posterior consistency -- Shrinkage prior -- Vector autoregressive models; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2018.1437043
• Languages: English
• ISSNs: 0162-1459

Abstract

ABSTRACT: Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among 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 nonhierarchical matrix-normal prior and a hierarchical prior, which corresponds to an arbitrary scale mixture of normals. We establish posterior consistency for both these priors under standard regularity assumptions, when the dimension p of the VAR model grows with the sample size n (but still remains smaller than n ). A special case corresponds to a shrinkage prior that introduces (group) sparsity in the columns of the model coefficient matrices. The performance of the model estimates are illustrated on synthetic and real macroeconomic datasets. Supplementary materials for this article are available online.