Bayesian model comparison for time-varying parameter VARs with stochastic volatility

Chan, JCC; Eisenstat, E

Bayesian model comparison for time-varying parameter VARs with stochastic volatility

Chan, JCC

Eisenstat, E

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Publication Type:: Journal Article
Citation:: Journal of Applied Econometrics, 2018, 33 (4), pp. 509 - 532
Issue Date:: 2018-06-01

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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.date.issued	2018-06-01	en_US
dc.identifier.citation	Journal of Applied Econometrics, 2018, 33 (4), pp. 509 - 532	en_US
dc.identifier.issn	0883-7252	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130029
dc.description.abstract	Copyright © 2018 John Wiley & Sons, Ltd. We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and the deviance information criterion (DIC) for time-varying parameter vector autoregressions (TVP-VARs), where both the regression coefficients and volatilities are drifting over time. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Using US data, we find overwhelming support for the TVP-VAR with stochastic volatility compared to a conventional constant coefficients VAR with homoskedastic innovations. Most of the gains, however, appear to have come from allowing for stochastic volatility rather than time variation in the VAR coefficients or contemporaneous relationships. Indeed, according to both criteria, a constant coefficients VAR with stochastic volatility outperforms the more general model with time-varying parameters.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE150100795
dc.relation.ispartof	Journal of Applied Econometrics	en_US
dc.relation.isbasedon	10.1002/jae.2617	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Bayesian model comparison for time-varying parameter VARs with stochastic volatility	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	33	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

Copyright © 2018 John Wiley & Sons, Ltd. We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and the deviance information criterion (DIC) for time-varying parameter vector autoregressions (TVP-VARs), where both the regression coefficients and volatilities are drifting over time. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Using US data, we find overwhelming support for the TVP-VAR with stochastic volatility compared to a conventional constant coefficients VAR with homoskedastic innovations. Most of the gains, however, appear to have come from allowing for stochastic volatility rather than time variation in the VAR coefficients or contemporaneous relationships. Indeed, according to both criteria, a constant coefficients VAR with stochastic volatility outperforms the more general model with time-varying parameters.

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http://hdl.handle.net/10453/130029