Shojaie Ali, Fox Emily B
Department of Biostatistics, University of Washington, Seattle, Washington 98195-4322, USA.
Department of Statistics, Stanford University, Stanford, California 94305-4020, USA.
Annu Rev Stat Appl. 2022 Mar;9(1):289-319. doi: 10.1146/annurev-statistics-040120-010930. Epub 2021 Nov 17.
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.
格兰杰因果关系在半个多世纪前被引入，现已成为分析许多应用领域中时间序列数据的常用工具，涵盖从经济学、金融学到基因组学和神经科学等领域。尽管很受欢迎，但该框架用于推断时间序列之间因果关系的有效性一直是持续争论的话题。此外，虽然最初的定义很通用，但计算工具的局限性使得格兰杰因果关系的应用主要局限于简单的二元向量自回归过程。本文首先回顾早期的发展和争论，然后讨论近期的进展，这些进展解决了早期方法的各种缺点，从高维时间序列模型到考虑非线性和非高斯观测值以及允许子采样和混合频率时间序列的最新发展。
