Results 11 to 20 of about 88,646 (292)

Granger causality revisited

NeuroImage, 2014
This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality.
Friston, Karl J., Bastos, André M., Oswal, Ashwini, van Wijk, Bernadette, Richter, Craig, Litvak, Vladimir   +5 more
openaire   +6 more sources

The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference [PDF]

Journal of Neuroscience Methods, 2014
Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences.
Barnett, Lionel, Seth, Anil K
openaire   +5 more sources

Analyzing Multiple Nonlinear Time Series with Extended Granger Causality [PDF]

Physics Letters, Section A: General, Atomic and Solid State Physics, 2004
Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations.
Arnhold, Bhattacharya, Boccaletti, Breakapear, Breakapear, Casdagli, Eckmann, Farmer, Fraser, Freiwald, Govindan Rangarajan, Granger, Jianfeng Feng, Kaminski, Kantz, Kennel, Lütkepohl, Mingzhou Ding, Priestley, Quian Quiroga, Quian Quiroga, Quyen, Sakata, Schiff, Schreiber, Wendling, Wiener, Wiesenfeldt, Yonghong Chen   +28 more
core   +4 more sources

Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data

Journal of Neuroscience Methods, 2006
It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose.
Bressler, Steven L., Chen, Yonghong, Ding, Mingzhou   +2 more
core   +3 more sources

Co-movement and Granger causality between Bitcoin and M2, inflation and economic policy uncertainty: Evidence from the U.K. and Japan

Heliyon, 2022
This study aims to investigate the co-movement and Granger causality between Bitcoin prices (BTC) and M2 (cash, demand, and time deposits), inflation, and economic policy uncertainty (EPU) in the U.K. and Japan.
Provash Kumer Sarker, Lei Wang
doaj   +1 more source

Measuring Granger Causality in Quantiles [PDF]

Journal of Business & Economic Statistics, 2020
We consider measures of Granger causality in quantiles, which detect and quantify both linear and nonlinear causal effects between random variables. The measures are based on nonparametric quantile regressions and defined as logarithmic functions of restricted and unrestricted expectations of quantile check loss functions.
Song, X., Taamouti, A.
openaire   +3 more sources

DLI: A Deep Learning-Based Granger Causality Inference

Complexity, 2020
Integrating autoencoder (AE), long short-term memory (LSTM), and convolutional neural network (CNN), we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference (DLI).
Wei Peng
doaj   +1 more source

The relationships between ASEAN stock markets: A spectral Granger causality approach

Tạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh, 2021
This article collects data of ASEAN6’s daily stock returns to investigate the relationships among them by traditional Granger causality test in combination with spectral Granger causality test.
Trần Thị Tuấn Anh
doaj   +1 more source

The Relation between Granger Causality and Directed Information Theory: A Review

Entropy, 2012
This report reviews the conceptual and theoretical links between Granger causality and directed information theory. We begin with a short historical tour of Granger causality, concentrating on its closeness to information theory.
Pierre-Olivier Amblard, Olivier J. J. Michel   +1 more
doaj   +1 more source

Meta-Granger Causality Testing [PDF]

SSRN Electronic Journal, 2015
Understanding the (causal) mechanisms at work is important for formulating evidence-based policy. But evidence from observational studies is often inconclusive with many studies finding conflicting results. In small to moderately sized samples, the outcome of Granger causality testing heavily depends on the lag length chosen for the underlying vector ...
Stephan B. Bruns, David I. Stern
openaire   +1 more source