Large Bayesian VARs: A flexible Kronecker error covariance structure

Joshua Chan

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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.

Keywords: stochastic volatility; non-Gaussian; ARMA; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C51 C53 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2015-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (32)

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Related works:
Journal Article: Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2015-41

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