Causal Inference: Juraj Bodík
From Belle Taylor on July 2nd, 2021
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Name: Juraj Bodík
Talk Title: Detection of causality in time series using extreme values
Abstract: We deal with the following problem: Let us have two stationary (possibly nonlinear) time series with heavy-tailed marginal distributions. We want to detect whether there is some Granger causality present. Even more, we want to determine the minimal lag, i.e. the time how much it takes for information to travel from one time series to another. We will examine the asymmetry in extremes between the cause and effect, and present a statistic that can estimate such asymmetries. The basis of the idea stands by the so-called causal tail coefficient for time series, which in some way represents the behaviour in extremes of one series conditioned on the presence of an extreme in the other.
This talk is a contributed talk at EVA 2021.