二元回归的速率无关Wald推断
Rate-Agnostic Wald Inference for Dyadic Regressions
- Emory University（埃默里大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出适用于二元回归的速率无关Wald推断方法，无需估计收敛速率，并引入半正定刀切统计量，通过蒙特卡洛实验和贸易引力应用验证其有效性。
AI中文摘要：
本文针对二元数据上的线性回归模型的最小二乘估计发展了Wald推断，适用于多个观测共享同一对单位的情形（例如，有向流、多层网络和二元面板）。我们证明，在关于依赖性累积的单一条件下，对于任意非随机的满秩约束序列，二元稳健Wald统计量渐近服从$\chi^2_q$分布。在整个过程中，不假设也不估计收敛速率，允许得分方差矩阵的条件数发散。我们进一步提出了一种删除一个单位的刀切替代方法，该方法通过构造保证半正定。在关于二元多重性的一个附加条件下，该刀切统计量达到相同的渐近极限，并且当该条件不满足时，保持渐近保守。补充材料包含所有证明、以异质速率收敛的估计系数的蒙特卡洛实验，以及一个应用于双边贸易的经验引力模型。
英文摘要：
This paper develops Wald inference for least-squares estimation of linear regression models on dyadic data, accommodating configurations where multiple observations share the same pair of units (e.g., directed flows, multilayer networks, and dyadic panels). We establish that the dyadic-robust Wald statistic is asymptotically $χ^2_q$ for an arbitrary nonrandom sequence of full-rank restrictions, under a single condition on the accumulation of dependence. Throughout, no convergence rate is assumed or estimated, permitting the condition number of the score's variance matrix to diverge. We further propose a delete-one-unit jackknife alternative that is positive semidefinite by construction. This jackknife statistic attains the same asymptotic limit under one additional condition on dyad multiplicity and remains asymptotically conservative when that condition fails. A supplement contains all proofs, Monte Carlo experiments featuring estimated coefficients that converge at heterogeneous rates, and an empirical gravity application to bilateral trade.
