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dc.contributor.authorGuo, Na
dc.contributor.authorZhang, Bo
dc.contributor.authorCross, Jamie
dc.date.accessioned2023-08-18T12:19:29Z
dc.date.available2023-08-18T12:19:29Z
dc.date.created2021-09-15T14:07:29Z
dc.date.issued2021
dc.identifier.citationJournal of Forecasting. 2021, 24 (2), 316-330.en_US
dc.identifier.issn0277-6693
dc.identifier.urihttps://hdl.handle.net/11250/3084841
dc.description.abstractWe investigate whether a class of trend models, which decompose a time series into an underlying trend and transitory component, with various error term structures can improve upon the forecast performance of commonly used time series models when forecasting consumer price index (CPI) inflation in Australia. The main result is that trend models tend to provide more accurate point and density forecasts at medium to long forecasting horizons compared with conventional autoregressive and Phillips curve models. The best medium‐term point forecasts come from a trend model with stochastic volatility in the transitory component and that with a moving average component, whereas long‐run point forecasts are better made by trend models with stochastic volatilities and a moving average component. In a full sample study, we also find that trend models can capture various dynamics in periods of significance to the Australian economy which conventional models cannot. This includes the dramatic reduction in inflation when the RBA adopted inflation targeting, a one‐off 10% Goods and Services Tax inflationary episode in 2000, and then gradually decline in inflation since 2014en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.subjecttrend modelen_US
dc.subjectinflation forecasten_US
dc.subjectBayesian analysisen_US
dc.subjectstochastic volatilityen_US
dc.titleTime-varying trend models for forecasting inflation in Australiaen_US
dc.title.alternativeTime-varying trend models for forecasting inflation in Australiaen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holderWileyen_US
dc.source.pagenumber316-330en_US
dc.source.volume24en_US
dc.source.journalJournal of Forecastingen_US
dc.source.issue2en_US
dc.identifier.doi10.1002/for.2814
dc.identifier.cristin1934583
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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