arXiv 20 Jun 2026 · Econometrics
arXiv:2606.22230 · PDF · DOI · OpenAlex · Extracted main text
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 complete characterization of predictive dependence. This paper develops a framework for distributional Granger causality based on a finite collection of channel-specific restrictions. Under suitable determinacy conditions, the channel menu is shown to be complete, yielding an identification result that links distributional Granger non-causality to a finite set of testable hypotheses. Building on this representation, we develop an adaptive sequential testing procedure that allocates inferential resources across channels while maintaining familywise error control through an alpha-investing mechanism. A policy-invariant validity theorem establishes finite-sample size control under arbitrary admissible selection rules, while an asymptotic efficiency theorem shows that a confidence-bound allocation rule achieves power equivalent to that of an infeasible oracle benchmark. The theoretical guarantees are derived from primitive mixing and moment conditions together with a circular-block permutation scheme.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Foster, D. P. and Stine, R. A (2008) $ $-Investing: A Procedure for Sequential Control of Expected False Discoveries | 0.843 | 3 | 3 | 100% |
| 2 | Aharoni, E. and Rosset, S (2014) Generalized $ $-Investing: Definitions, Optimality Results and Application to Public Databases | 0.644 | 2 | 2 | 100% |
| 3 | Barnett, L. and Barrett, A. B. and Seth, A. K (2009) Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables | 0.644 | 2 | 2 | 100% |
| 4 | Bouezmarni, T. and Rombouts, J. V. K. and Taamouti, A (2012) Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality | 0.644 | 2 | 2 | 100% |
| 5 | Cheung, Y.-W. and Ng, L. K (1996) A Causality-in-Variance Test and Its Application to Financial Market Prices | 0.644 | 2 | 2 | 100% |
| 6 | Diks, C. and Panchenko, V (2006) A New Statistic and Practical Guidelines for Nonparametric Granger Causality Testing | 0.644 | 2 | 2 | 100% |
| 7 | Geweke, J (1982) Measurement of Linear Dependence and Feedback Between Multiple Time Series | 0.644 | 2 | 2 | 100% |
| 8 | Geweke, J (1984) Measures of Conditional Linear Dependence and Feedback Between Time Series | 0.644 | 2 | 2 | 100% |
| 9 | Granger, C. W. J (1969) Investigating Causal Relations by Econometric Models and Cross-Spectral Methods | 0.644 | 2 | 2 | 100% |
| 10 | Hiemstra, C. and Jones, J. D (1994) Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.