Granger Causality: A Review and Recent Advances
It provides an incremental update for researchers in fields like economics and neuroscience, summarizing debates and extending applicability to more complex data scenarios.
This paper reviews Granger causality, a method for analyzing time series data, and discusses recent advances that address limitations such as high-dimensionality, nonlinearity, and non-Gaussian observations, though no specific numerical results are provided.
Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience. Despite this popularity, the validity of this notion for inferring causal relationships among time series has remained the topic of continuous debate. Moreover, while the original definition was general, limitations in computational tools have primarily limited the applications of Granger causality to simple bivariate vector auto-regressive processes or pairwise relationships among a set of variables. Starting with a review of early developments and debates, this paper discusses recent advances that address various shortcomings of the earlier approaches, from models for high-dimensional time series to more recent developments that account for nonlinear and non-Gaussian observations and allow for sub-sampled and mixed frequency time series.