论文标题
推断没有平滑的大面板，具有横截面和时间依赖性
Inference without smoothing for large panels with cross-sectional and temporal dependence
论文作者
论文摘要
本文在存在未知形式的横截面和时间依赖性的情况下解决了大面板数据模型的推断。我们有兴趣进行推断，这些推论不依赖于任何平滑参数的选择，就像经常使用的协方差矩阵“ HAC”估计器一样。为此，我们为不需要选择带宽或平滑参数选择的估计器和有效的自举方案的渐近协方差的群集估计器，并适应时间和横截面依赖性的非参数性质。我们的方法基于这样的观察，即固定效应面板数据模型的频谱表示使误差在时间上近似不相关。我们提出的引导程序可以看作是频域中的野生引导程序。我们提供了一些蒙特卡罗模拟，以阐明我们推论程序的样本表现较小。
This paper addresses inference in large panel data models in the presence of both cross-sectional and temporal dependence of unknown form. We are interested in making inferences that do not rely on the choice of any smoothing parameter as is the case with the often employed "HAC" estimator for the covariance matrix. To that end, we propose a cluster estimator for the asymptotic covariance of the estimators and valid bootstrap schemes that do not require the selection of a bandwidth or smoothing parameter and accommodate the nonparametric nature of both temporal and cross-sectional dependence. Our approach is based on the observation that the spectral representation of the fixed effect panel data model is such that the errors become approximately temporally uncorrelated. Our proposed bootstrap schemes can be viewed as wild bootstraps in the frequency domain. We present some Monte-Carlo simulations to shed some light on the small sample performance of our inferential procedure.
