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Modeling and Forecasting the Long Memory of Cyclical Trends in Paleoclimate Data

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorBarrio Castro, Tomás del
dc.contributor.authorEscribano, Álvaro
dc.contributor.authorSibbertsen, Philipp
dc.contributor.editorUniversidad Carlos III. Departamento de Economíaes
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia y Universidades (España)
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2024-06-18T09:48:14Z
dc.date.available2024-06-18T09:48:14Z
dc.date.issued2024-06-17
dc.description.abstractThis paper identifies and estimates the relevant cycles in paleoclimate data of earth temperature, ice volume and CO2. Cyclical cointegration analysis is used to connect these cycles to the earth eccentricity and obliquity and to see that the earth surface temperature and ice volume are closely connected. These findings are used to build a forecasting model including the cyclical component as well as the relevant earth and climate variables which outperforms models ignoring the cyclical behaviour of the data. Especially the turning points can be predicted accurately using the proposed approach. Out of sample forecasts for the turning points of earth temperature, ice volume and CO2 are derived.en
dc.description.sponsorshipThe authors are grateful to Josu Arteche, Jennifer Castle, Liudas Giraitis, Jesus Gonzalo, David Hendry, Yeliz Özer and the participants of the conference on Climate Finance in Hannover 2023, the Luxemburg Time Series Workshop 2024, the Workshop on Time Series Econometrics 2024 in Zaragossa and the IAAE 2024 in Thessaloniki for helpful comments and discussion. Tomas del Barrio Castro gratefully acknowledges financial support from project PID2020-114646RB-C430 funded by MCIN/AEI /10.13039/501100011033. Alvaro Escribano gratefully acknowledges financial support by MICIN/ AEI/10.13039/50110001Agencia Estatal de Investigacion-Ministerio de Ciencia e Innovacion, (Maria de Maeztu); MICIN/AEI/2023/00378/001 and CEX2021-001181-M; CEX2021-001181-M financed by MICIU/AEI/10.13039/501100011033 and Comunidad de Madrid, grant EPUC3M11 (V PRICIT). Philipp SIbbertsen gratefully acknowlwdges financial support by Deutsche Forschungsgemeinschaft under grant 258395632en
dc.format.extent32
dc.identifier.issn2340-5031
dc.identifier.urihttps://hdl.handle.net/10016/43987
dc.identifier.uxxiDT/0000002151
dc.language.isoeng
dc.relation.ispartofseriesWorking paper. Economicsen
dc.relation.ispartofseries24-12
dc.relation.projectIDGobierno de España. PID2020-114646RB-C430es
dc.relation.projectIDGobierno de España. CEX2021-001181-Mes
dc.relation.projectIDComunidad de Madrid. EPUC3M11es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEconomíaes
dc.subject.otherPaleoclimate Cyclesen
dc.subject.otherCyclical Fractional Cointegrationen
dc.subject.otherForecasting Climate Dataen
dc.titleModeling and Forecasting the Long Memory of Cyclical Trends in Paleoclimate Dataen
dc.typeworking paper en
dc.type.hasVersionVoRen
dspace.entity.typePublicationen
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