论文标题
分组数据模型的光谱和光谱后估计器
Spectral and post-spectral estimators for grouped panel data models
论文作者
论文摘要
在本文中，我们为分组数据模型开发了光谱和光谱后估计器。在渐近学中，这两个估计器都是一致的，在渐近学上，观测值$ n $和时间段$ t $同时生长大。此外，后光谱估计器为$ \ sqrt {nt} $ - 一致且渐近地正常，平均为零，即使$ t $的越来越慢，却是分离良好的组。因此，后光谱估计器具有与Bonhomme和Manresa（2015）开发的分组固定效应估计量相当的理论属性。但是，与分组的固定效应估计量相反，我们的光谱后估计器在计算上是直接的。
In this paper, we develop spectral and post-spectral estimators for grouped panel data models. Both estimators are consistent in the asymptotics where the number of observations $N$ and the number of time periods $T$ simultaneously grow large. In addition, the post-spectral estimator is $\sqrt{NT}$-consistent and asymptotically normal with mean zero under the assumption of well-separated groups even if $T$ is growing much slower than $N$. The post-spectral estimator has, therefore, theoretical properties that are comparable to those of the grouped fixed-effect estimator developed by Bonhomme and Manresa (2015). In contrast to the grouped fixed-effect estimator, however, our post-spectral estimator is computationally straightforward.
