Large Bayesian Vector Autoregressions

Chan, JCC

Large Bayesian Vector Autoregressions

Chan, JCC

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Publication Type:: Chapter
Citation:: Advanced Studies in Theoretical and Applied Econometrics, 2020, 52 pp. 95 - 125
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.date.accessioned	2020-03-16T13:56:41Z
dc.date.available	2020-03-16T13:56:41Z
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	Advanced Studies in Theoretical and Applied Econometrics, 2020, 52 pp. 95 - 125	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139350
dc.description.abstract	© 2020, Springer Nature Switzerland AG. Bayesian vector autoregressions are widely used for macroeconomic forecasting and structural analysis. Until recently, however, most empirical work had considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a variety of shrinkage priors that are useful for tackling the parameter proliferation problem in large Bayesian VARs. This is followed by a detailed discussion of efficient sampling methods for overcoming the computational problem. We then give an overview of some recent models that incorporate various important model features into conventional large Bayesian VARs, including stochastic volatility, non-Gaussian, and serially correlated errors. Efficient estimation methods for fitting these more flexible models are then discussed. These models and methods are illustrated using a forecasting exercise that involves a real-time macroeconomic dataset. The corresponding Matlab code is also provided [Matlab code is available at http://joshuachan.org/].	en_US
dc.relation.ispartof	Advanced Studies in Theoretical and Applied Econometrics	en_US
dc.relation.isbasedon	10.1007/978-3-030-31150-6_4	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Large Bayesian Vector Autoregressions	en_US
dc.type	Chapter
utslib.citation.volume	52	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	*
utslib.copyright.embargo	2022-01-01T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

© 2020, Springer Nature Switzerland AG. Bayesian vector autoregressions are widely used for macroeconomic forecasting and structural analysis. Until recently, however, most empirical work had considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a variety of shrinkage priors that are useful for tackling the parameter proliferation problem in large Bayesian VARs. This is followed by a detailed discussion of efficient sampling methods for overcoming the computational problem. We then give an overview of some recent models that incorporate various important model features into conventional large Bayesian VARs, including stochastic volatility, non-Gaussian, and serially correlated errors. Efficient estimation methods for fitting these more flexible models are then discussed. These models and methods are illustrated using a forecasting exercise that involves a real-time macroeconomic dataset. The corresponding Matlab code is also provided [Matlab code is available at http://joshuachan.org/].

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http://hdl.handle.net/10453/139350