Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors

Basit Öğe Kaydı
dc.authorwosidYazgan, Ege/GPG-1135-2022
dc.contributor.authorSoybilgen, Baris
dc.contributor.authorYazgan, Ege
dc.date.accessioned2024-07-18T20:40:39Z
dc.date.available2024-07-18T20:40:39Z
dc.date.issued2021
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this study, we nowcast quarter-over-quarter US GDP growth rates between 2000Q2 and 2018Q4 using tree-based ensemble machine learning models, namely, bagged decision trees, random forests, and stochastic gradient tree boosting. To solve the ragged edge problem and reduce the dimension of the data set, we adopt a dynamic factor model. Dynamic factors extracted from 10 groups of financial and macroeconomic variables are fed to machine learning models for nowcasting US GDP. Our results show that tree-based ensemble models usually outperform linear dynamic factor models. Factors obtained from real variables appear to be more influential in machine learning models. The impact of factors derived from financial and price variables can only become important in predicting GDP after the great financial crisis of 2008-9, reflecting the effect extra loose monetary policies implemented in the period following the crisis.en_US
dc.identifier.doi10.1007/s10614-020-10083-5
dc.identifier.endpage417en_US
dc.identifier.issn0927-7099
dc.identifier.issn1572-9974
dc.identifier.issue1en_US
dc.identifier.pmid33437130en_US
dc.identifier.scopus2-s2.0-85099108148en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage387en_US
dc.identifier.urihttps://doi.org/10.1007/s10614-020-10083-5
dc.identifier.urihttps://hdl.handle.net/11411/7165
dc.identifier.volume57en_US
dc.identifier.wosWOS:000605928800003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofComputational Economicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBaggingen_US
dc.subjectBoostingen_US
dc.subjectDynamic Factor Modelen_US
dc.subjectMachine Learningen_US
dc.subjectNowcastingen_US
dc.subjectRandom Forestsen_US
dc.subjectNumberen_US
dc.subjectOutputen_US
dc.titleNowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factorsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Web of Science Indexed Publications
PubMed Indexed Publications
Scopus Indexed Publications