High-Dimensional Granger Causality for Climatic Attribution

TL;DR

This paper introduces a high-dimensional Granger causality framework for climate data, enabling the analysis of complex causal networks among radiative forcings and global temperatures without pre-testing for unit roots.

Contribution

It develops a novel high-dimensional VAR approach that estimates causal relations directly in levels, accommodating stochastic trends and long memory in climate time series.

Findings

Established causal networks linking radiative forcings to temperatures

Connected radiative forcings among themselves to trace dynamic effects

Avoided biases from unit-root and cointegration tests

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.