New study reveals groundbreaking insights into predicting causal relationships accurately.
The article shows that when there is no Granger causality, a specific estimator follows a certain distribution. This distribution can be approximated by another one. This also applies to a different type of causality estimator. The researchers provide a way to test for Granger causality without knowing the model order. They discuss how this method can be used in real-life situations. The analysis can be extended to other types of causality tests. The researchers also talk about ways to estimate the distribution of the estimator when there is causality.
