Network autocorrelation models (NAMs) are widely used to study a response variable of interest among subjects embedded within a network. Although the NAM is highly useful for studying such networked observational units, several simulation studies have raised concerns about point estimation. Specifically, these studies have consistently demonstrated a negative bias of maximum likelihood estimators (MLEs) of the network effect parameter. However, in order to gain a practical understanding of point estimation in the NAM, these findings need to be expanded in three important ways. First, these simulation studies are based on relatively simple network generative models rather than observed networks, thereby leaving as an open question how realistic network topologies may affect point estimation in practice. Second, although there has been strong work done in developing two-stage least squares estimators as well as Bayesian estimators, only the MLE has received extensive attention in the literature, thus leaving practitioners in question as to best practices. Third, the performance of these estimators need to be compared using both bias and variance, as well as the coverage rate of each estimator's corresponding confidence or credible interval. In this paper we describe a simulation study which aims to overcome these shortcomings in the following way. We first fit real social networks using the exponential random graph model and used the Bayesian predictive posterior distribution to generate networks with realistic topologies. We then compared the performance of the three different estimators mentioned above.

网络自相关模型（NAM）被广泛用于研究网络中嵌入的主题之间感兴趣的响应变量。尽管NAM对于研究这种网络化的观测单位非常有用，但是一些模拟研究引起了对点估计的关注。具体而言，这些研究始终证明了网络效应参数的最大似然估计器（MLE）的负偏差。但是，为了对NAM中的点估计有实际的了解，需要以三种重要方式扩展这些发现。首先，这些模拟研究基于相对简单的网络生成模型，而不是基于观察到的网络，因此作为一个悬而未决的问题，现实的网络拓扑如何在实践中会影响点估计。第二，尽管在开发两阶段最小二乘估计器和贝叶斯估计器方面已经做了大量工作，但是只有MLE在文献中受到广泛关注，因此使从业人员对最佳实践存有疑问。第三，需要使用偏差和方差以及每个估算器的相应置信度或可信区间的覆盖率来比较这些估算器的性能。在本文中，我们描述了一个模拟研究，旨在通过以下方式克服这些缺点。我们首先使用指数随机图模型拟合真实的社交网络，然后使用贝叶斯预测后验分布生成具有现实拓扑的网络。然后，我们比较了上述三种不同估算器的性能。