A non-parametric variable selection for Structural VARs The post Granger Causal Networks and Indirect Feedback appeared first on Towards Data Science .
Summary generated by The Flow from the publisher's feed. The full article lives at Towards Data Science.
arXiv:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.
arXiv:2407. 09632v3 Announce Type: replace-cross Abstract: We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series.
arXiv:2607. 20696v1 Announce Type: new Abstract: We propose CEDAR (Causal Edge Discovery for Autoregressive Processes), a constraint-based method for lagged causal edge discovery in sparse autoregressive time series.
arXiv:2603. 20980v3 Announce Type: replace Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable.
The simplest most important idea for time series forecasting The post Measuring Structure Stability of Econometric Models appeared first on Towards Data Science .
arXiv:2606. 18834v1 Announce Type: new Abstract: Causal discovery methods commonly assume that all data is independently and identically distributed (i.