Predicting Global Temperature Anomaly: A Definitive Investigation Using an Ensemble of Twelve Competing Forecasting Models

Hossein Hassani, Emmanuel Silva (), Rangan Gupta and Sonali Das ()
Additional contact information
Emmanuel Silva: Statistical Research Centre, The Business School, Bournemouth University, UK

No 201561, Working Papers from University of Pretoria, Department of Economics

Abstract: In this paper we analyze whether (anthropometric) CO2 can forecast global temperature anomaly (GT) over an annual out-of-sample period of 1907-2012, using an in-sample of 1880- 1906. For our purpose, we use 12 parametric and non-parametric univariate (only comprising of GT) and multivariate (including both GT and CO2) models. Our results show that the Horizontal Multivariate Singular Spectral Analysis (HMSSA) models (both Recurrent (-R) and Vector (-V)) consistently outperform the other competing models. More importantly, from the performance of the HMSSA-R model, we find conclusive evidence that CO2 can forecast GT, and predict its direction of change. Our results highlight the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process, i.e., linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.

Keywords: Global temperature anomaly; CO2 emissions; Forecasting; Univariate and multivariate models (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 Q53 Q54 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2015-08
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models (2018)
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:pre:wpaper:201561

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().