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Modelling Low-Frequency Covariability of Paleoclimatic Data

Author

Listed:
  • Vasco J.Gabriel
  • Luis F. Martins
  • Anthoulla Phella

Abstract

This paper explores formal statistical procedures that allow us to quantify low-frequency comovement amongst a range of paleoclimate times series. Our first contribution is methodological: we extend the long-run covariability approach of Muller and Watson (2018) to higher dimensional settings by means of a first-pass partialling out of exogenous sources of variation. Our second contribution is empirical: we provide new estimates for the long-run relationship between temperatures and CO2, concluding that in the long-run a 100ppm increase in CO2 levels would raise temperatures around 1◦C. Finally, we illustrate how joint modelling of this set of paleoclimate time series can be carried out by factor analysis and how long-term projections about temperature increases and ice-sheet retreat can be constructed.

Suggested Citation

  • Vasco J.Gabriel & Luis F. Martins & Anthoulla Phella, 2021. "Modelling Low-Frequency Covariability of Paleoclimatic Data," Working Papers 2022_17, Business School - Economics, University of Glasgow.
    • Handle: RePEc:gla:glaewp:2022_17
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    More about this item

    Keywords

    Paleoclimate data; Glaciar cycles; Equilibrium climate sensitivity; Low frequency analysis.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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