EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

High-Dimensional Granger Causality for Climatic Attribution

Luca Margaritella1, Marina Friedrich2, and Stephan Smeekes3
Luca Margaritella et al.
  • 1Maastricht University, Department of Quantitative Economics, Maastricht, The Netherlands (
  • 2Vrije Universiteit Amsterdam, Department of Econometrics and Data Science, Amsterdam, The Netherlands (
  • 3Maastricht University, Department of Quantitative Economics, Maastricht, The Netherlands (

We use the framework of Granger-causality testing in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global as well as hemispheric temperatures. By allowing for high dimensionality in the model we can enrich the information set with all relevant natural and anthropogenic forcing variables to obtain reliable causal relations. These variables have mostly been investigated in an aggregated form or in separate models in the previous literature. An additional advantage of our framework is that it allows to ignore the order of integration of the variables and to directly estimate the VAR in levels, therefore avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are often argued to contain specific stochastic trends as well as yielding long memory. We are thus able to display the causal networks linking radiative forcings to global and hemispheric temperatures but also to causally connect radiative forcings among themselves, therefore allowing for a careful reconstruction of a timeline of causal effects among forcings. The robustness of our proposed procedure makes it an important tool for policy evaluation in tackling global climate change.

How to cite: Margaritella, L., Friedrich, M., and Smeekes, S.: High-Dimensional Granger Causality for Climatic Attribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8020,, 2021.


Display file