Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247623 
Year of Publication: 
2021
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 9 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-18
Publisher: 
MDPI, Basel
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 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.
Subjects: 
climate econometrics
global warming
information flows
vector autoregressions
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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