Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Chan, Joshua C. C.
• Is Part Of: Journal of business & economic statistics. Volume 38:Issue 1(2020)
• Page Start: 68
• Page End: 79
• Publication Date: 2020-01-02
• Subjects: ARMA -- Forecasting -- Non-Gaussian -- Stochastic volatility.; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2018.1451336
• Languages: English
• ISSNs: 0735-0015

Abstract

ABSTRACT: We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-Gaussian, heteroscedastic, and serially dependent innovations. To make estimation computationally tractable, we exploit a certain Kronecker structure of the likelihood implied by this class of models. We propose a unified approach for estimating these models using Markov chain Monte Carlo (MCMC) methods. In an application that involves 20 macroeconomic variables, we find that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance.