Two-Step Estimation of Incomplete Information Social Interaction Models With Sample Selection
Two-Step Estimation of Incomplete Information Social Interaction Models With Sample Selection
复制标题
DOI：
10.1080/07350015.2017.1394861
复制
发表时间：
2018-10
影响因子：
3
通讯作者：
Tadao Hoshino
中科院分区：
文献类型：
--
作者：
Tadao Hoshino
摘要本文考虑了不完全信息下的线性社会互动模型，该模型允许由于样本选择而缺失结果数据。对于模型估计，假设每个个体基于理性预期形成他/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。证明了估计量的相合性和渐近正态性。作为一个实证说明，我们应用所提出的模型和方法，以全国青少年健康纵向研究（添加健康）的数据来检验友谊互动对青少年的学业成就的影响。我们提供的实证证据表明，交互作用是重要的决定因素的平均成绩和控制样本选择偏差有一定的影响估计结果。本文的补充材料可在网上查阅。
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.