标题： 因子增强线性模型的估计及其在学生学业数据中的应用 显示英文标题

标题： Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data

摘要： 在许多纵向研究中，经济理论并未指导从业者必须施加哪些限制以解决因子增强线性模型的旋转不确定性。我们研究了这个问题，并提出了使用内部生成工具的识别方面的几个新结果。我们提出了一类新的估计量，并利用聚类样本和高维模型的最新进展建立了大样本结果。我们进行了模拟研究，结果表明所提出的方法在估计未知因子方面优于现有方法。最后，我们考虑了三个实证应用，使用了在小学、中学和大学不同学科中聚类的学生行政数据。 显示英文摘要

摘要： In many longitudinal settings, economic theory does not guide practitioners on the type of restrictions that must be imposed to solve the rotational indeterminacy of factor-augmented linear models. We study this problem and offer several novel results on identification using internally generated instruments. We propose a new class of estimators and establish large sample results using recent developments on clustered samples and high-dimensional models. We carry out simulation studies which show that the proposed approaches improve the performance of existing methods on the estimation of unknown factors. Lastly, we consider three empirical applications using administrative data of students clustered in different subjects in elementary school, high school and college.