Dyadic regression models are commonly analyzed under the conventional dyadic dependence framework, where two observations may be dependent only if the corresponding dyads share a node. This paper studies inference when nodes are ordered and nearby nodes are exposed to common latent shocks, so that dyads with no shared endpoint may still be dependent. Although each additional covariance term may be weak, the number of nearby-node dyad pairs grows with the sample size, making their aggregate contribution asymptotically non-negligible. We develop an inferential framework for dyadic arrays with ordered-node dependence and propose two variance estimators: a dependent-node dyadic cluster-robust variance estimator that retains covariance terms between dyads with nearby endpoints, and a row-column moving-block jackknife method that deletes adjacent blocks of nodes together with all dyads touching those nodes. We establish the asymptotic validity of both procedures under weak dependence along the ordered node index. Monte Carlo evidence shows improvements in size control, with the jackknife procedure displaying comparatively stable finite-sample performance. An application to international trade gravity regressions shows that accounting for ordered-node dependence substantially weakens the statistical evidence for free trade agreement effects.

翻译：二值回归模型通常在传统二值依赖框架下进行分析，即仅当两个观测对应的二值共享一个节点时才可能存在依赖关系。本文研究了节点有序且相邻节点面临共同潜在冲击时的推断问题，在此情形下，无共同端点的二值对之间也可能存在依赖关系。尽管每个额外的协方差项可能较弱，但相邻节点二值对的数量随样本量增长，其整体贡献在渐近意义上不可忽略。我们针对具有有序节点依赖的二值对阵列发展了一套推断框架，并提出了两种方差估计量：一种依赖节点二值聚类稳健方差估计量，保留了端点相邻的二值对之间的协方差项；另一种行列移动分块刀切法，通过删除相邻节点区块及所有与该节点接触的二值来实现。我们证明了在有序节点索引的弱依赖条件下两种方法的渐近有效性。蒙特卡洛证据显示，该方法在控制检验尺寸方面有所改进，其中刀切法在有限样本下表现出相对稳定的性能。对国际贸易引力回归的应用表明，考虑有序节点依赖显著削弱了自由贸易协定效应的统计证据。