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On Spurious Causality, CO 2 , and Global Temperature

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  • Philippe Goulet Coulombe

    (Département des Sciences Économiques, Université du Québec à Montréal, Montréal, QC H2L 2C4, Canada)

  • Maximilian Göbel

    (Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal)

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.

Suggested Citation

  • Philippe Goulet Coulombe & Maximilian Göbel, 2021. "On Spurious Causality, CO 2 , and Global Temperature," Econometrics, MDPI, vol. 9(3), pages 1-18, September.
  • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:3:p:33-:d:630626
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    References listed on IDEAS

    as
    1. Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.
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    3. Jochem Marotzke & Piers M. Forster, 2015. "Forcing, feedback and internal variability in global temperature trends," Nature, Nature, vol. 517(7536), pages 565-570, January.
    4. William Nordhaus, 2014. "Estimates of the Social Cost of Carbon: Concepts and Results from the DICE-2013R Model and Alternative Approaches," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(1), pages 000.
    5. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    6. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, October.
    7. Ladislav Kristoufek & Paulo Ferreira, 2018. "Capital asset pricing model in Portugal: Evidence from fractal regressions," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(3), pages 173-183, November.
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