非转移效用下二元网络形成模型中的逻辑差分法
Logical Differencing in Dyadic Network Formation Models with Nontransferable Utilities
AI总结：
针对非转移效用下二元网络形成模型的加性不可分离性问题，提出逻辑差分法消除未观测个体异质性，给出一致估计量并通过仿真和Nyakatoke风险分担网络验证了方法有效性。
摘要：
本文研究非转移效用（NTU）下的二元网络形成半参数模型。NTU在需要双边同意的现实社会互动中十分常见，但其本质会引发加性不可分离性。我们提出了一种名为logical differencing（逻辑差分）的新方法，展示了在不满足加性可分离性的条件下，如何消除模型中未观测到的个体异质性。该方法的核心思路是基于多元单调性，构建涉及未观测异质性的两个互斥约束的交集事件。我们给出了一个一致估计量，通过仿真分析了其性能，并将该方法应用于Nyakatoke风险分担网络。
英文摘要：
This paper considers a semiparametric model of dyadic network formation under nontransferable utilities (NTU). NTU arises frequently in real-world social interactions that require bilateral consent, but by its nature induces additive non-separability. We show how unobserved individual heterogeneity in our model can be canceled out without additive separability, using a novel method we call logical differencing. The key idea is to construct events involving the intersection of two mutually exclusive restrictions on the unobserved heterogeneity, based on multivariate monotonicity. We provide a consistent estimator and analyze its performance via simulation, and apply our method to the Nyakatoke risk-sharing networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
