Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure

Chan, JCC

Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure

Chan, JCC

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Publication Type:: Journal Article
Citation:: Journal of Business and Economic Statistics, 2020, 38 (1), pp. 68 - 79
Issue Date:: 2020-01-02

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.date.accessioned	2020-04-14T21:16:31Z
dc.date.available	2020-04-14T21:16:31Z
dc.date.issued	2020-01-02	en_US
dc.identifier.citation	Journal of Business and Economic Statistics, 2020, 38 (1), pp. 68 - 79	en_US
dc.identifier.issn	0735-0015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139991
dc.description.abstract	© 2020, © 2020 American Statistical Association. 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.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE150100795
dc.relation.ispartof	Journal of Business and Economic Statistics	en_US
dc.relation.isbasedon	10.1080/07350015.2018.1451336	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Econometrics	en_US
dc.title	Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	38	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	open_access	*
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

© 2020, © 2020 American Statistical Association. 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.

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http://hdl.handle.net/10453/139991