Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autorschaft

  • Tomas del Barrio Castro
  • Alvaro Escribano
  • Philipp Sibbertsen

Organisationseinheiten

Externe Organisationen

  • University of the Balearic Islands
  • Universität Carlos III zu Madrid
Plum Print visual indicator of research metrics
  • Captures
    • Readers: 2
see details

Details

OriginalspracheEnglisch
Aufsatznummer108520
Seitenumfang17
FachzeitschriftEnergy economics
Jahrgang147
Frühes Online-Datum11 Mai 2025
PublikationsstatusVeröffentlicht - Juni 2025

Abstract

This 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 behavior 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.

ASJC Scopus Sachgebiete

Zitieren

Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data. / Castro, Tomas del Barrio; Escribano, Alvaro; Sibbertsen, Philipp.
in: Energy economics, Jahrgang 147, 108520, 06.2025.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Castro TDB, Escribano A, Sibbertsen P. Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data. Energy economics. 2025 Jun;147:108520. Epub 2025 Mai 11. doi: 10.1016/j.eneco.2025.108520
Castro, Tomas del Barrio ; Escribano, Alvaro ; Sibbertsen, Philipp. / Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data. in: Energy economics. 2025 ; Jahrgang 147.
Download
@article{b834a4e2eae8494aa507eee3ce55d1c0,
title = "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data",
abstract = "This 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 behavior 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.",
keywords = "Cyclical fractional cointegration, Forecasting climate data, Paleoclimate cycles",
author = "Castro, {Tomas del Barrio} and Alvaro Escribano and Philipp Sibbertsen",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors",
year = "2025",
month = jun,
doi = "10.1016/j.eneco.2025.108520",
language = "English",
volume = "147",
journal = "Energy economics",
issn = "0140-9883",
publisher = "Elsevier BV",

}

Download

TY - JOUR

T1 - Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data

AU - Castro, Tomas del Barrio

AU - Escribano, Alvaro

AU - Sibbertsen, Philipp

N1 - Publisher Copyright: © 2025 The Authors

PY - 2025/6

Y1 - 2025/6

N2 - This 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 behavior 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.

AB - This 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 behavior 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.

KW - Cyclical fractional cointegration

KW - Forecasting climate data

KW - Paleoclimate cycles

UR - http://www.scopus.com/inward/record.url?scp=105004873187&partnerID=8YFLogxK

U2 - 10.1016/j.eneco.2025.108520

DO - 10.1016/j.eneco.2025.108520

M3 - Article

AN - SCOPUS:105004873187

VL - 147

JO - Energy economics

JF - Energy economics

SN - 0140-9883

M1 - 108520

ER -