Identifying US business cycle regimes using dynamic factors and neural network models

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dc.contributor.authorSoybilgen, Barış
dc.date.accessioned2021-06-29T12:16:28Z
dc.date.available2021-06-29T12:16:28Z
dc.date.issued2020-08-01
dc.description.abstractWe use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.1002/for.2658en_US
dc.identifier.scopus2-s2.0-85079733589en_US
dc.identifier.urihttps://hdl.handle.net/11411/3909
dc.identifier.urihttps://doi.org/10.1002/for.2658
dc.identifier.wosWOS:000516807900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue5en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors1en_US
dc.pages827-840en_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofJournal of Forecastingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectbusiness cycleen_US
dc.subjectdynamic factor modelen_US
dc.subjectneural networken_US
dc.subjectrecessionen_US
dc.titleIdentifying US business cycle regimes using dynamic factors and neural network modelsen_US
dc.typeArticleen_US
dc.volume39en_US

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