Estimating Earth's Temperature Response with Transformed and Augmented OLS

Justin Sun

Papers from arXiv.org

Abstract: The long-term relationship between radiative forcing and surface temperature is central to predicting the impacts of climate change. This study employs multicointegration to characterize this relationship and provides the first application of the Transformed and Augmented Ordinary Least Squares (TAOLS) estimator to estimate the multicointegration model. The main objective is to estimate the Equilibrium Climate Sensitivity (ECS), defined as the global mean surface temperature increase following a doubling of atmospheric carbon dioxide. Diagnostic tests reveal that radiative forcing innovations are strongly non-Gaussian, providing a key motivation for applying semiparametric TAOLS rather than the parametric Gaussian maximum likelihood method. TAOLS is also robust to misspecification of the short-run dynamics. Using the three data pairings of Bruns et al. (2020), we obtain TAOLS estimates of ECS ranging from $1.81^{\circ}$C to $2.49^{\circ}$C, consistently below their main maximum likelihood estimate of $2.80^{\circ}$C. Because the model we use is linear and is based on global-mean data, it cannot capture state-dependent feedbacks or regional differences; extending TAOLS along either dimension is a natural next step.

Date: 2026-03, Revised 2026-08
New Economics Papers: this item is included in nep-env
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2603.13766 Latest version (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:arx:papers:2603.13766

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().