This module provides function to estimate causal influences of signals on each
other.
Granger causality
Granger causality is a method to determine causal influence of one signal on
another based on autoregressive modelling. It was developed by Nobel prize
laureate Clive Granger and has been adopted in various numerical fields ever
since [gr1]. In its simplest form, the
method tests whether the past values of one signal help to reduce the
prediction error of another signal, compared to the past values of the latter
signal alone. If it does reduce the prediction error, the first signal is said
to Granger cause the other signal.
Limitations
The user must be mindful of the method’s limitations, which are assumptions of
covariance stationary data, linearity imposed by the underlying autoregressive
modelling as well as the fact that the variables not included in the model will
not be accounted for [gr2].
Implementation
The mathematical implementation of Granger causality methods in this module
closely follows [gr3].