Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts

Zhang, B; Chan, JCC; Cross, JL

Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts

Zhang, B Chan, JCC Cross, JL

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: International Journal of Forecasting, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zhang, B
dc.contributor.author	Chan, JCC
dc.contributor.author	Cross, JL
dc.date.accessioned	2020-06-15T19:50:59Z
dc.date.available	2020-06-15T19:50:59Z
dc.date.issued	2020-01-01
dc.identifier.citation	International Journal of Forecasting, 2020
dc.identifier.issn	0169-2070
dc.identifier.uri	http://hdl.handle.net/10453/141432
dc.description.abstract	© 2020 International Institute of Forecasters We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence, which results in standard Kalman filter techniques not being directly applicable. To overcome this hurdle, we develop an efficient posterior simulator that builds on recently developed precision-based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy, and the U.S.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	International Journal of Forecasting
dc.relation.isbasedon	10.1016/j.ijforecast.2020.01.004
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0104 Statistics, 1403 Econometrics, 1505 Marketing
dc.subject.classification	Econometrics
dc.title	Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts
dc.type	Journal Article
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
utslib.for	1505 Marketing
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-06-15T19:50:55Z
pubs.publication-status	Published

Abstract:

© 2020 International Institute of Forecasters We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence, which results in standard Kalman filter techniques not being directly applicable. To overcome this hurdle, we develop an efficient posterior simulator that builds on recently developed precision-based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy, and the U.S.

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http://hdl.handle.net/10453/141432