Granger causality and path diagrams for multivariate time series

DOI10.1016/j.jeconom.2005.06.032zbMath1360.62455OpenAlexW2065454200MaRDI QIDQ276915

Michael Eichler

Publication date: 4 May 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2005.06.032

zbMATH Keywords

Granger causalitygraphical modelsmultivariate time seriesspurious causalitycausal inference

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Causality analysis of futures sugar prices in Zhengzhou based on graphical models for multivariate time series ⋮ Causal conditioning and instantaneous coupling in causality graphs ⋮ Measuring frequency domain Granger causality for multiple blocks of interacting time series ⋮ Convergence of covariance and spectral density estimates for high-dimensional locally stationary processes ⋮ Dynamic network reconstruction from heterogeneous datasets ⋮ Conditional independence graph for nonlinear time series and its application to international financial markets ⋮ Monotone dependence in graphical models for multivariate Markov chains ⋮ A note on marginal and conditional independence ⋮ Graphical modeling of stochastic processes driven by correlated noise ⋮ The impact of latent confounders in directed network analysis in neuroscience ⋮ Causal inference with multiple time series: principles and problems ⋮ Graphical models for multivariate Markov chains ⋮ Directed graphs and variable selection in large vector autoregressive models ⋮ Statistical causality for multivariate nonlinear time series via Gaussian process models ⋮ An Algebraic Estimator for Large Spectral Density Matrices ⋮ Graphical models for nonstationary time series ⋮ Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions ⋮ Graphical modelling of multivariate time series ⋮ Multiple testing and error control in Gaussian graphical model selection ⋮ Learning Minimal Latent Directed Information Polytrees ⋮ Detection of information flow in major international financial markets by interactivity network analysis ⋮ Granger causality between vectors of time series: a puzzling property ⋮ Detecting groups in large vector autoregressions ⋮ Testing nonparametric and semiparametric hypotheses in vector stationary processes ⋮ Identification of nonlinear VAR models using general conditional independence graphs ⋮ Geometric and long run aspects of Granger causality ⋮ Unifying Markov properties for graphical models ⋮ Bayesian learning of graphical vector autoregressions with unequal lag-lengths ⋮ On Granger causality and the effect of interventions in time series ⋮ Testing coefficients of AR and bilinear time series models by a graphical approach ⋮ Multivariate Time Series Analysis ⋮ Learning causal graphs of nonlinear structural vector autoregressive model using information theory criteria ⋮ Causality and separability

Uses Software

• TETRAD
• StFinMetrics

Cites Work

• Causation, prediction, and search
• Marginalizing and conditioning in graphical models
• A Linear Theory for Noncausality
• Causal diagrams for empirical research
• On the Granger Condition for Non-Causality
• Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions
• Markov Properties for Acyclic Directed Mixed Graphs
• Short Run and Long Run Causality in Time Series: Theory
• On the Validity of the Markov Interpretation of Path Diagrams of Gaussian Structural Equations Systems with Correlated Errors
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Graphical interaction models for multivariate time series.
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item