二元网络形成模型中效用转移性的计量经济学
The Econometrics of Utility Transferability in Dyadic Network Formation Models
AI总结：
本文研究如何估计个体对与其他个体建立联系的偏好，比较了在假设效用可转移和不可转移情况下估计的难度，并探讨了回归变量对称性对最大似然估计的影响，提供了检验可转移效用假设的规范检验。
摘要：
本文研究了如何估计个体在网络中与其他个体建立联系的偏好。它比较了在假设效用可转移和不可转移情况下估计的难度，并在对称和非对称回归变量的情况下探讨了最大似然估计的稳健性。当配对特定回归变量对称时，假设可转移效用（TU-MLE）的最大似然估计的充分条件也适用于不假设可转移效用（NTU-MLE）的最大似然估计。当回归变量不对称时，本文提供了NTU-MLE的一致性和渐近正态性的充分条件。本文还提供了一个规范检验来评估可转移效用假设的有效性。来自不同经济领域的两个应用展示了结果的价值。本文发现当可转移效用假设被违反时，研究人员使用TU-MLE；当无法拒绝可转移效用假设时，研究人员使用基于NTU模型的估计器。
英文摘要：
This paper studies how to estimate an individual's taste for forming a connection with another individual in a network. It compares the difficulty of estimation with and without the assumption that utility is transferable between individuals, and with and without the assumption that regressors are symmetric across individuals in the pair. I show that when pair-specific regressors are symmetric, the sufficient conditions for consistency and asymptotic normality of the maximum likelihood estimator that assumes transferable utility (TU-MLE) are also sufficient for the maximum likelihood estimator that does not assume transferable utility (NTU-MLE). When regressors are asymmetric, I provide sufficient conditions for the consistency and asymptotic normality of the NTU-MLE. I also provide a specification test to assess the validity of the transferable utility assumption. Two applications from different fields of economics demonstrate the value of my results. I find evidence of researchers using the TU-MLE when the transferable utility assumption is violated, and evidence of researchers using NTU-model-based estimators when the validity of the transferable utility assumption cannot be rejected.
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。
