论文标题
具有未观察到线性异质性的半参数网络形成模型
A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity
论文作者
论文摘要
本文分析了在未观察到的特异性异质性的存在下网络形成的半参数模型。目的是在未观察到的因子的分布未指定的情况下，识别和估计与同质性属性相关的偏好参数。本文为网络形成的文献提供了两个主要贡献。首先，它为依赖于特殊阻遏物的存在的参数向量建立了一个新的点标识结果。识别证明是建设性的，并表征了感兴趣的参数的封闭形式。其次，它引入了一个简单的两步半参数估计器，该估计器与第一步内核估计器的参数向量。估计器在计算上是可触及的，并且可以应用于密集和稀疏网络。此外，我表明估计器是一致的，并且随着网络中的个体数量的增加而具有限制正态分布。蒙特卡洛实验表明，估计器在有限样本和具有不同稀疏度不同的网络中表现良好。
This paper analyzes a semiparametric model of network formation in the presence of unobserved agent-specific heterogeneity. The objective is to identify and estimate the preference parameters associated with homophily on observed attributes when the distributions of the unobserved factors are not parametrically specified. This paper offers two main contributions to the literature on network formation. First, it establishes a new point identification result for the vector of parameters that relies on the existence of a special repressor. The identification proof is constructive and characterizes a closed-form for the parameter of interest. Second, it introduces a simple two-step semiparametric estimator for the vector of parameters with a first-step kernel estimator. The estimator is computationally tractable and can be applied to both dense and sparse networks. Moreover, I show that the estimator is consistent and has a limiting normal distribution as the number of individuals in the network increases. Monte Carlo experiments demonstrate that the estimator performs well in finite samples and in networks with different levels of sparsity.
