  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.
