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New framework for distributional Granger causality developed

By PulseAugur Editorial · [1 sources] ·

A new framework for distributional Granger causality has been developed, extending beyond the conditional mean to analyze predictive dependence in time series. This approach considers distributional features like scale, tail behavior, and asymmetry, which are crucial outside the Gaussian setting. The framework enables identification of causal content through channel-specific restrictions and introduces an adaptive sequential testing procedure that controls for familywise error rates using an alpha-investing mechanism. AI

RANK_REASON The item is an academic paper detailing a new statistical framework and testing procedure. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New framework for distributional Granger causality developed

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The item is an academic paper detailing a new statistical framework and testing procedure. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ayush Jha ·

    Distributional Granger Causality: Identification, Sequential Inference, and Adaptive Testing

    Predictive dependence in time series need not be confined to the conditional mean. Outside the Gaussian setting, causal content may arise through conditional scale, tail behavior, asymmetry, or other distributional features, implying that no single Granger-type test provides a co…

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