Boosting nonlinear predictability of macroeconomic time series

dc.contributor.authorKauppi Heikki
dc.contributor.authorVirtanen Timo
dc.contributor.organizationfi=taloustiede|en=Economics|
dc.contributor.organization-code1.2.246.10.2458963.20.17691981389
dc.converis.publication-id48817701
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/48817701
dc.date.accessioned2022-10-28T14:05:35Z
dc.date.available2022-10-28T14:05:35Z
dc.description.abstract<p>We apply the boosting estimation method in order to investigate to what extent and at what horizons macroeconomic time series have nonlinear predictability that comes from their own history. Our results indicate that the U.S. macroeconomic time series have more exploitable nonlinear predictability than previous studies have found. On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals.<br></p>
dc.format.pagerange151
dc.format.pagerange170
dc.identifier.eissn1872-8200
dc.identifier.jour-issn0169-2070
dc.identifier.olddbid186214
dc.identifier.oldhandle10024/169308
dc.identifier.urihttps://www.utupub.fi/handle/11111/34316
dc.identifier.urnURN:NBN:fi-fe2021042825050
dc.language.isoen
dc.okm.affiliatedauthorKauppi, Heikki
dc.okm.affiliatedauthorVirtanen, Timo
dc.okm.discipline511 Economicsen_GB
dc.okm.discipline511 Kansantaloustiedefi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherElsevier
dc.publisher.countryNetherlandsen_GB
dc.publisher.countryAlankomaatfi_FI
dc.publisher.country-codeNL
dc.relation.doi10.1016/j.ijforecast.2020.03.008
dc.relation.ispartofjournalInternational Journal of Forecasting
dc.relation.issue1
dc.relation.volume37
dc.source.identifierhttps://www.utupub.fi/handle/10024/169308
dc.titleBoosting nonlinear predictability of macroeconomic time series
dc.year.issued2021

Tiedostot

Näytetään 1 - 1 / 1
Ladataan...
Name:
boosting_nonlinear_predictability_2019.pdf
Size:
841.88 KB
Format:
Adobe Portable Document Format
Description:
Final draft