Artikel
On spurious causality, CO2, and global temperature
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 CO2 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.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 3 ; Pages: 1-18 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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climate econometrics
global warming
information flows
vector autoregressions
- Event
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Geistige Schöpfung
- (who)
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Goulet Coulombe, Philippe
Göbel, Maximilian
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2021
- DOI
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doi:10.3390/econometrics9030033
- Handle
- Last update
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10.03.2025, 11:46 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Goulet Coulombe, Philippe
- Göbel, Maximilian
- MDPI
Time of origin
- 2021
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