Large Bayesian VARMAs

Chan, JCC; Eisenstat, E; Koop, G

Large Bayesian VARMAs

Chan, JCC

Eisenstat, E Koop, G

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Publication Type:: Journal Article
Citation:: Journal of Econometrics, 2016, 192 (2), pp. 374 - 390
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Eisenstat, E	en_US
dc.contributor.author	Koop, G	en_US
dc.date.issued	2016-06-01	en_US
dc.identifier.citation	Journal of Econometrics, 2016, 192 (2), pp. 374 - 390	en_US
dc.identifier.issn	0304-4076	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121926
dc.description.abstract	© 2016 Elsevier B.V. All rights reserved. Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.	en_US
dc.relation.ispartof	Journal of Econometrics	en_US
dc.relation.isbasedon	10.1016/j.jeconom.2016.02.005	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Large Bayesian VARMAs	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	192	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied Economics	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	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	192	en_US

Abstract:

© 2016 Elsevier B.V. All rights reserved. Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121926