Identifying noise shocks

Benati, L; Chan, J; Eisenstat, E; Koop, G

Identifying noise shocks

Benati, L Chan, J

Eisenstat, E Koop, G

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Publication Type:: Journal Article
Citation:: Journal of Economic Dynamics and Control, 2020, 111
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Benati, L	en_US
dc.contributor.author	Chan, J 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.accessioned	2020-03-16T13:55:24Z
dc.date.available	2020-03-16T13:55:24Z
dc.date.issued	2020-02-01	en_US
dc.identifier.citation	Journal of Economic Dynamics and Control, 2020, 111	en_US
dc.identifier.issn	0165-1889	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139349
dc.description.abstract	© 2019 Elsevier B.V. We study identifying restrictions that allow news and noise shocks to be recovered empirically within a Bayesian structural VARMA framework. In population, the identification scheme we consider exactly recovers news and noise shocks. Monte Carlo evidence further demonstrates its excellent performance, as it recovers the key features of the postulated data-generation process—the real-business cycle model of Barsky and Sims (2011) augmented with noise shocks about future total factor productivity (TFP)—with great precision. In an empirical application, evidence suggests that TFP noise shocks play a minor role in macroeconomic fluctuations.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180102373
dc.relation.ispartof	Journal of Economic Dynamics and Control	en_US
dc.relation.isbasedon	10.1016/j.jedc.2019.103780	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Economics	en_US
dc.title	Identifying noise shocks	en_US
dc.type	Journal Article
utslib.citation.volume	111	en_US
utslib.for	1401 Economic Theory	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1502 Banking, Finance and Investment	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	2022-02-01T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	111	en_US

Abstract:

© 2019 Elsevier B.V. We study identifying restrictions that allow news and noise shocks to be recovered empirically within a Bayesian structural VARMA framework. In population, the identification scheme we consider exactly recovers news and noise shocks. Monte Carlo evidence further demonstrates its excellent performance, as it recovers the key features of the postulated data-generation process—the real-business cycle model of Barsky and Sims (2011) augmented with noise shocks about future total factor productivity (TFP)—with great precision. In an empirical application, evidence suggests that TFP noise shocks play a minor role in macroeconomic fluctuations.

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http://hdl.handle.net/10453/139349