Francesco Bravo, Degui Li, Zheng Li
arXiv 18 Oct 2026 · Econometrics
arXiv:2610.25009 · PDF · Extracted main text
This paper considers nonparametric quantile regression for directed dyadic data observed on the ordered pairs of a complete network with n vertices. The observations exhibit dyadic dependence: dyads with disjoint indices are independent, whereas dyads sharing one or two indices are correlated. The local linear smoothing technique is adopted to estimate the quantile regression function. The asymptotic theory reveals that convergence rates of the proposed dyadic quantile regression estimation crucially depend on whether degeneracy occurs for the underlying network generating process. A rate-adaptive standardized version of the limit distribution is derived under some mild conditions. In addition, an easy-to-implement method is proposed to estimate the asymptotic variance of nonparametric dyadic quantile regression estimation. The finite-sample performance and applicability of the proposed methodology are illustrated with Monte-Carlo simulation and a real data application to bilateral portfolio holdings.
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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 | Li et al (2021) Nonparametric quantile regression estimation with mixed discrete and continuous data self | 1.000 | 5 | 4 | 100% |
| 2 | Graham et al (2021) Minimax risk and uniform convergence rates for nonparametric dyadic regression | 0.941 | 6 | 4 | 83% |
| 3 | Graham et al (2024) Kernel density estimation for undirected dyadic data | 0.941 | 6 | 3 | 83% |
| 4 | Cai \ Xu (2008) Nonparametric quantile estimations for dynamic smooth coefficient models | 0.843 | 3 | 3 | 100% |
| 5 | Graham (2020) Dyadic regression | 0.843 | 3 | 3 | 100% |
| 6 | Yu \ Jones (1998) Local linear quantile regression | 0.843 | 3 | 3 | 100% |
| 7 | Cattaneo et al (2024) Uniform inference for kernel density estimators with dyadic data | 0.737 | 3 | 2 | 100% |
| 8 | Santos Silva \ Tenreyro (2006) The log of gravity | 0.737 | 3 | 2 | 100% |
| 9 | Graham (2020) Network data | 0.644 | 2 | 2 | 100% |
| 10 | Menzel (2021) Bootstrap with cluster-dependence in two or more dimensions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.