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

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  • Castro, Tomas del Barrio
  • Escribano, Alvaro
  • Sibbertsen, Philipp

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.

Suggested Citation

  • Castro, Tomas del Barrio & Escribano, Alvaro & Sibbertsen, Philipp, 2025. "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data," Energy Economics, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:eneeco:v:147:y:2025:i:c:s0140988325003445
    DOI: 10.1016/j.eneco.2025.108520
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    1. Bauwens, Luc & Chevillon, Guillaume & Laurent, Sébastien, 2023. "We modeled long memory with just one lag!," Journal of Econometrics, Elsevier, vol. 236(1).
    2. Josu Arteche & Peter M. Robinson, 2000. "Semiparametric Inference in Seasonal and Cyclical Long Memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 1-25, January.
    3. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).
    4. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    5. Castle, Jennifer L. & Hendry, David F., 2020. "Climate Econometrics: An Overview," Foundations and Trends(R) in Econometrics, now publishers, vol. 10(3-4), pages 145-322, August.
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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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