Semiparametric Dynamic Logit Model with Endogenous Networks
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内生网络下的半参数动态 Logit 模型
- 研究问题
- 当行为持续性同时来自习惯与随时间变化的未观测特征，而这些特征也影响交友时，如何利用网络区分其作用？
- 方法与思路
- 作者比较交友形成方式相同的个体，假定其共享未观测社会影响及其变化；据此构造精确或近似匹配估计，并以 jackknife 修正近似匹配偏差。
- 材料陈述的贡献
- 摘要称推导了两种匹配情形的渐近性质；模拟中估计偏差较所列对照方法小，区间覆盖接近名义水平，并以青少年吸烟数据示例。
- 阅读关注点
- 核对交友形成方式相同的定义、共享社会影响及其变化的限制，以及匹配误差、偏差修正和渐近推断所需的网络条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未完整列出识别和渐近条件，模拟表现不能推广至任意内生网络。
原文摘要
Many choices persist over time and are made among friends. Teenagers who smoke tend to keep smoking, and they tend to have friends who smoke. Part of this persistence is habit, but part may come from characteristics researchers do not observe, such as a taste for risk, that shape both whether a student smokes and whom they befriend. Methods that treat friendships as given cannot separate the two when the characteristic changes over time. This paper shows how the friendship network itself can, in a dynamic logit model. Two people who form friendships in the same way are assumed to share the same unobserved influence on the outcome, the social influence function, so comparing them cancels it, even when that influence changes between waves, provided the change is shared by people who form friendships in the same way. No specific model of how friendships form is assumed. I propose an estimator built on this comparison and derive its large-sample behavior in two cases: exact matching, when the characteristic and the covariates take few distinct values, and approximate matching, with a jackknife correction for the bias it creates. In simulations, the estimator removes most of the bias left by peer-average controls and by the standard panel method that compares each person only with themselves over time, and its confidence intervals have coverage close to the correct level. An application to adolescent smoking in the Glasgow Teenage Friends and Lifestyle Study illustrates the method and the data it requires.