Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Chan, JCC

Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Chan, JCC

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: International Journal of Forecasting, 2021, 37, (3), pp. 1212-1226
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Chan, JCC
dc.date.accessioned	2022-02-08T19:14:52Z
dc.date.available	2022-02-08T19:14:52Z
dc.date.issued	2021-07-01
dc.identifier.citation	International Journal of Forecasting, 2021, 37, (3), pp. 1212-1226
dc.identifier.issn	0169-2070
dc.identifier.uri	http://hdl.handle.net/10453/154312
dc.description.abstract	Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	International Journal of Forecasting
dc.relation.isbasedon	10.1016/j.ijforecast.2021.01.002
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1403 Econometrics, 1505 Marketing
dc.subject.classification	Econometrics
dc.title	Minnesota-type adaptive hierarchical priors for large Bayesian VARs
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
utslib.for	1505 Marketing
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	*
dc.date.updated	2022-02-08T19:14:50Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	3

Abstract:

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.

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http://hdl.handle.net/10453/154312