不可转移效用下二元网络形成模型的矩约束

中文摘要 AI 辅助

本文研究了在不可转移效用下，具有未观测个体异质性的二元网络形成模型中矩约束的构建问题。利用五节点五元组中的观测链接和协变量，我们构建了不依赖于个体固定效应的矩约束。对于一大类协变量设定，该构建在意义上是最小的，即使用了最少数量的节点和二元组。基于这些矩约束，我们提出了五元组GMM估计量。我们建立了五元组GMM估计量在稠密、稀疏和超稀疏网络机制下的渐近正态性，并给出了各机制特有的收敛速度和渐近方差。这些结果为所有三种机制下的推断提供了基础。为了提高方法的计算效率，我们开发了一种算法，将估计量的计算成本从朴素的O(N^5)降低到O(N^3)。我们将所提出的方法应用于一个学术讨论网络，发现了学术同质性以及链接形成与潜在合作伙伴对不同视角的开放性之间的正相关关系。

英文摘要

This paper investigates the construction of moment restrictions in dyadic network formation models with unobserved individual heterogeneity under nontransferable utility. Using observed links and covariates from five-node pentads, we construct moment restrictions that do not depend on individual fixed effects. For a broad class of covariate specifications, the construction is minimal in the sense that it uses the least possible number of nodes and dyads. Based on these moment restrictions, we propose the pentad-GMM estimator. We establish asymptotic normality of the pentad-GMM estimator in dense, sparse, and ultra-sparse network regimes, with regime-specific convergence rates and asymptotic variances. These results provide a basis for inference across all three regimes. To make our method computationally efficient, we develop an algorithm that reduces the computational cost of the estimator from naïve \(O(N^5)\) to \(O(N^3)\). We apply the proposed method to an academic-discussion network, and find academic homophily and a positive association between link formation and a potential partner's openness to different perspectives.

发表机构

• The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
• Renmin University of China（中国人民大学）

机构由 AI 辅助整理，请以论文原文为准。