Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

Li, M; Mendieta-Muñoz, I

Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

Li, M

Mendieta-Muñoz, I

Permalink

Publisher:: Berkeley Electronic Press
Publication Type:: Journal Article
Citation:: Studies in Nonlinear Dynamics and Econometrics, 2022, 26, (3), pp. 337-359
Issue Date:: 2022-06-03

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 3 Jun 2023

Adobe PDF

Download Published versionAdobe PDF (294.23 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Li, M https://orcid.org/0000-0002-8807-7277
dc.contributor.author	Mendieta-Muñoz, I
dc.date.accessioned	2022-11-07T03:02:43Z
dc.date.available	2022-11-07T03:02:43Z
dc.date.issued	2022-06-03
dc.identifier.citation	Studies in Nonlinear Dynamics and Econometrics, 2022, 26, (3), pp. 337-359
dc.identifier.issn	1081-1826
dc.identifier.issn	1558-3708
dc.identifier.uri	http://hdl.handle.net/10453/163273
dc.description.abstract	We propose a structural representation of the correlated unobserved components model, which allows for a structural interpretation of the interactions between trend and cycle shocks. We show that point identification of the full contemporaneous matrix which governs the structural interaction between trends and cycles can be achieved via heteroskedasticity. We develop an efficient Bayesian estimation procedure that breaks the multivariate problem into a recursion of univariate ones. An empirical implementation for the US Phillips curve shows that our model is able to identify the magnitude and direction of spillovers of the trend and cycle components both within-series and between-series.
dc.language	English
dc.publisher	Berkeley Electronic Press
dc.relation.ispartof	Studies in Nonlinear Dynamics and Econometrics
dc.relation.isbasedon	10.1515/snde-2020-0027
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 1403 Econometrics
dc.subject.classification	Econometrics
dc.title	Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0102 Applied Mathematics
utslib.for	1403 Econometrics
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	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-06-03T00:00:00+1000Z
dc.date.updated	2022-11-07T03:02:40Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	3

Abstract:

We propose a structural representation of the correlated unobserved components model, which allows for a structural interpretation of the interactions between trend and cycle shocks. We show that point identification of the full contemporaneous matrix which governs the structural interaction between trends and cycles can be achieved via heteroskedasticity. We develop an efficient Bayesian estimation procedure that breaks the multivariate problem into a recursion of univariate ones. An empirical implementation for the US Phillips curve shows that our model is able to identify the magnitude and direction of spillovers of the trend and cycle components both within-series and between-series.

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

http://hdl.handle.net/10453/163273