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Annual Review of Statistics and Its Application

Volume 9, 2022

Granger Causality: A Review and Recent Advances

Abstract

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 framework 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 constrained the applications of Granger causality to primarily simple bivariate vector autoregressive processes. Starting with a review of early developments and debates, this article 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 subsampled and mixed-frequency time series.

Keyword(s): deep neural networks, graphical models, mixed-frequency time series, multivariate time series, penalized estimation, vector autoregressive model
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2022-03-07
2024-04-25
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Granger Causality: A Review and Recent Advances
Annual Review of Statistics and Its Application 9, 289 (2022); https://doi.org/10.1146/annurev-statistics-040120-010930
/content/journals/10.1146/annurev-statistics-040120-010930
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