Unobserved components with stochastic volatility in U.S. inflation: Estimation and signal extraction

Li, M; Koopman, SJ

Unobserved components with stochastic volatility in U.S. inflation: Estimation and signal extraction

Li, M

Koopman, SJ

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Journal of Applied Econometrics, 2018, 36, (5), pp. 614-627
Issue Date:: 2018-02-02

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Field	Value	Language
dc.contributor.author	Li, M https://orcid.org/0000-0002-8807-7277
dc.contributor.author	Koopman, SJ
dc.date.accessioned	2022-03-31T13:25:05Z
dc.date.available	2022-03-31T13:25:05Z
dc.date.issued	2018-02-02
dc.identifier.citation	Journal of Applied Econometrics, 2018, 36, (5), pp. 614-627
dc.identifier.issn	0883-7252
dc.identifier.issn	1099-1255
dc.identifier.uri	http://hdl.handle.net/10453/155805
dc.description.abstract	The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation-based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	Journal of Applied Econometrics
dc.relation.isbasedon	10.1002/jae.2831
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1402 Applied Economics, 1403 Econometrics
dc.subject.classification	Econometrics
dc.title	Unobserved components with stochastic volatility in U.S. inflation: Estimation and signal extraction
dc.type	Journal Article
utslib.citation.volume	36
utslib.for	1402 Applied Economics
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
dc.date.updated	2022-03-31T13:25:04Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	36
utslib.citation.issue	5

Abstract:

The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation-based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications.

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http://hdl.handle.net/10453/155805