Boucher Vincent, Mourifié Ismael
Department of Economics, Université Laval, 1025, Avenue des Sciences-Humaines, Quebec City, Quebec, G1V 0A6, Canada.
Department of Economics, University of Toronto, 150 St George Street, Toronto, Ontario, M5S 3G7, Canada.
Econom J. 2017 Oct;20(3):S14-S46. doi: 10.1111/ectj.12096. Epub 2017 Oct 20.
We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a set of parameters from the individuals' utility functions using the observation of a single, but large, social network. We show that, under some conditions, a simple logit-based estimator is coherent, consistent and asymptotically normally distributed under a weak version of homophily. The approach is compelling as the computing time is minimal and the estimator can be easily implemented using pre-programmed estimators available in most statistical packages. We provide an application of our method using the Add Health database.
我们探讨了在非常大的群体中网络形成的战略模型的渐近性质。具体而言，我们关注（无向）指数随机图模型。我们希望通过观察单个但规模较大的社会网络，从个体的效用函数中恢复出一组参数。我们表明，在某些条件下，一个基于简单对数几率的估计量在弱同质性版本下是连贯的、一致的且渐近正态分布的。该方法很有吸引力，因为计算时间最短，并且可以使用大多数统计软件包中可用的预编程估计量轻松实现该估计量。我们使用“增加健康”数据库对我们的方法进行了应用。
