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High-Dimensional Granger Causality for Climatic Attribution

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  • Marina Friedrich
  • Luca Margaritella
  • Stephan Smeekes

Abstract

In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global temperatures. By allowing for high dimensionality in the model, we can enrich the information set with relevant natural and anthropogenic forcing variables to obtain reliable causal relations. This provides a step forward from existing climatology literature, which has mostly treated these variables in isolation in small models. Additionally, our framework allows to disregard the order of integration of the variables by directly estimating the VAR in levels, thus avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are well known to contain stochastic trends and long memory. We are thus able to establish causal networks linking radiative forcings to global temperatures and to connect radiative forcings among themselves, thereby allowing for tracing the path of dynamic causal effects through the system.

Suggested Citation

  • Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.
  • Handle: RePEc:arx:papers:2302.03996
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    References listed on IDEAS

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    1. Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.
    2. David W. J. Thompson & John J. Kennedy & John M. Wallace & Phil D. Jones, 2008. "A large discontinuity in the mid-twentieth century in observed global-mean surface temperature," Nature, Nature, vol. 453(7195), pages 646-649, May.
    3. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe & Rudebusch, Glenn D. & Zhang, Boyuan, 2021. "Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1509-1519.
    4. Umberto Triacca & Alessandro Attanasio & Antonello Pasini, 2013. "Anthropogenic global warming hypothesis: testing its robustness by Granger causality analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 24(4), pages 260-268, June.
    5. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    6. Hansheng Wang & Bo Li & Chenlei Leng, 2009. "Shrinkage tuning parameter selection with a diverging number of parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 671-683, June.
    7. Robert K. Kaufmann & David I. Stern, 1997. "Evidence for human influence on climate from hemispheric temperature relations," Nature, Nature, vol. 388(6637), pages 39-44, July.
    8. John A. Church & Neil J. White & Julie M. Arblaster, 2005. "Significant decadal-scale impact of volcanic eruptions on sea level and ocean heat content," Nature, Nature, vol. 438(7064), pages 74-77, November.
    9. Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2020. "A statistical model of the global carbon budget," CREATES Research Papers 2020-18, Department of Economics and Business Economics, Aarhus University.
    10. Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
    11. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    12. Smeekes, Stephan & Taylor, A.M. Robert, 2012. "Bootstrap Union Tests For Unit Roots In The Presence Of Nonstationary Volatility," Econometric Theory, Cambridge University Press, vol. 28(2), pages 422-456, April.
    13. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(1), pages 21-59, February.
    14. Park, Joon Y. & Phillips, Peter C.B., 1988. "Statistical Inference in Regressions with Integrated Processes: Part 1," Econometric Theory, Cambridge University Press, vol. 4(3), pages 468-497, December.
    15. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    16. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    17. David Stern & Robert Kaufmann, 2014. "Anthropogenic and natural causes of climate change," Climatic Change, Springer, vol. 122(1), pages 257-269, January.
    18. Pretis, Felix, 2020. "Econometric modelling of climate systems: The equivalence of energy balance models and cointegrated vector autoregressions," Journal of Econometrics, Elsevier, vol. 214(1), pages 256-273.
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