Modelling Low-Frequency Covariability of Paleoclimatic Data

Vasco Gabriel, Luis F. Martins and Anthoulla Phella

Working Papers from Business School - Economics, University of Glasgow

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.

Keywords: Paleoclimate data; Glaciar cycles; Equilibrium climate sensitivity; Low frequency analysis. (search for similar items in EconPapers)
JEL-codes: C22 C53 Q54 (search for similar items in EconPapers)
Date: 2021-12
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.gla.ac.uk/media/Media_1045851_smxx.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2022_17

Access Statistics for this paper

More papers in Working Papers from Business School - Economics, University of Glasgow Contact information at EDIRC.
Bibliographic data for series maintained by Business School Research Team ().