ABSTRACT

This article considers linear social interaction models under incomplete information that allow for missing outcome data due to sample selection. For model estimation, assuming that each individual forms his/her belief about the other members’ outcomes based on rational expectations, we propose a two-step series nonlinear least squares estimator. Both the consistency and asymptotic normality of the estimator are established. As an empirical illustration, we apply the proposed model and method to National Longitudinal Study of Adolescent Health (Add Health) data to examine the impacts of friendship interactions on adolescents’ academic achievements. We provide empirical evidence that the interaction effects are important determinants of grade point average and that controlling for sample selection bias has certain impacts on the estimation results. Supplementary materials for this article are available online.

摘要

本文考虑了不完全信息下的线性社交互动模型，该模型允许由于样本选择而导致缺少结果数据。对于模型估计，假设每个人都基于理性预期形成了他/她对其他成员结果的信念，我们提出了一个两步序列非线性最小二乘估计器。建立了估计量的一致性和渐近正态性。作为经验例证，我们将提出的模型和方法应用于国家青少年健康纵向研究（添加健康）数据，以检查友谊互动对青少年学业成绩的影响。我们提供的经验证据表明，相互作用的影响是平均成绩的重要决定因素，控制样本选择偏差对估计结果有一定影响。