论文标题
使用学生成就数据的应用程序估算因子增强的线性模型
Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data
论文作者
论文摘要
在许多纵向环境中，经济理论并未引导从业者了解必须施加的限制类型，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，并为使用内部生成的仪器提供了一些有关识别的新结果。我们提出了一类新的估计量，并使用群集样品和高维模型的最新发展建立了大型样本结果。我们进行仿真研究，表明所提出的方法改善了现有方法对未知因素的估计的性能。最后，我们使用在小学，高中和大学聚集在不同学科的学生的行政数据考虑三个经验应用。
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.
