On Spurious Causality, CO 2, and Global Temperature

Philippe Goulet Coulombe and Maximilian Göbel
Additional contact information
Maximilian Göbel: Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal

Econometrics, 2021, vol. 9, issue 3, 1-18

Abstract: Stips et al. (2016) use information flows (Liang (2008, 2014)) to establish causality from various forcings to global temperature. We show that the formulas being used hinge on a simplifying assumption that is nearly always rejected by the data. We propose the well-known forecast error variance decomposition based on a Vector Autoregression as an adequate measure of information flow, and find that most results in Stips et al. (2016) cannot be corroborated. Then, we discuss which modeling choices (e.g., the choice of CO 2 series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series.

Keywords: information flows; vector autoregressions; global warming; climate econometrics (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2225-1146/9/3/33/pdf (application/pdf)
https://www.mdpi.com/2225-1146/9/3/33/ (text/html)

Related works:
Working Paper: On Spurious Causality, CO2, and Global Temperature (2021)
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:gam:jecnmx:v:9:y:2021:i:3:p:33-:d:630626

Access Statistics for this article

Econometrics is currently edited by Ms. Jasmine Liu

More articles in Econometrics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().