基于观测多层网络数据的回归分析
Regression with Observational Multilayered Network Data
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中文总结 AI 辅助
本文提出基于两层复用网络数据的广义三阶段最小二乘（G3SLS）估计量，用于估计线性均值回归模型的社会效应系数，经蒙特卡洛验证其性能良好，实证发现经济学顶级期刊论文间存在显著引用同行效应。
中文摘要 AI 辅助
本文提出了一种新方法，利用非实验多维网络数据来估计社会科学中常用的“线性均值回归模型”中的社会效应系数。该方法通过利用相同观测对象间另一组传统意义上外生的网络关联，可容纳与模型误差相关的社会互动。具体而言，本文假设两层复用网络数据结构完全可观测，以此提出一种新的广义三阶段最小二乘（G3SLS）估计量，该估计量具有一致性、渐近正态性，且因其闭式定义，可通过广泛使用的现有统计软件轻松实现。其基础假设足够通用，可容纳观测数据的常见问题，如测量误差、内生性（同时性）和未观测异质性。蒙特卡洛模拟验证了所提G3SLS估计量在这些场景下的良好小样本性能。一项实证应用发现，发表在经济学顶级综合期刊的研究论文之间存在正向且显著的引用同行效应。
英文摘要
A novel method to estimate social effect coefficients in the popular so-called linear-in-means regression model in the Social Sciences is presented here that utilizes non-experimental multidimensional network data. The procedure can accommodate social interactions that correlate with the error in the model by making use of a different set of network links among the same observations that are exogenous in the traditional sense. In particular, the full observability of a two-layered multiplex network data structure is assumed here to propose a new Generalized 3-Stage Least Squares (G3SLS) estimator that is consistent, asymptotically normally distributed, and also easy to implement using widely-used existing statistical software because of its closed-form definition. The underlying assumptions are general enough to accommodate common problems with observational data such as measurement error, simultaneity, and unobserved heterogeneity. Monte Carlo exercises confirm the good small sample performance of the proposed G3SLS estimator in these scenarios. An empirical application finds positive and significant peer effects in citations among research articles published in top general-interest journals in economics.
