论文标题
面板回归的因子和因子加载增强估计器
Factor and factor loading augmented estimators for panel regression
论文作者
论文摘要
本文考虑了线性面板数据模型，其中回归器和未观察到的依赖性是通过因子结构建模的。渐近设置使得时间段的数量和样本量都转到无穷大。允许非施加因素，并且因样本量而增长到无限的因素。我们研究回归系数的两步估计器。第一步，估计因子和因子负荷。然后，第二步对应于回归器上结果的面板回归以及第一步的因子和因子负荷的估计。第一步可以使用不同的方法，而第二步是唯一的。我们在第一步的估计器和数据生成过程中得出了足够的条件，在该过程中，两步估计器在渐近上是正常的。在第一步中使用基于主成分分析的方法的假设也得出了渐近正常的估计器。两步程序在模拟中表现出良好的有限样本特性。
This paper considers linear panel data models where the dependence of the regressors and the unobservables is modelled through a factor structure. The asymptotic setting is such that the number of time periods and the sample size both go to infinity. Non-strong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations.
