Bootstrap inference for panel data models wirh serial and cross sectional dependence of unknown form

本程序的第一部分考虑了面板数据回归模型的Bootstrap方法，该模型估计了固定效应。特别地，我发展了一种新的面板回归模型的Bootstrap方法，该方法对误差项中未知形式的序列和截面相关性具有稳健性。我称它为面板块自举方法，因为它对收集每个时间段的横截面单位的向量块进行重采样。它是Driskoll和Kraay(1998)空间和序列相关面板数据模型协方差估计的Bootstrap模拟。初步模拟结果表明，该方法具有良好的有限样本性质，且优于Driskoll和Kraay(1998)的稳健标准误差。在与Driskoll和Kraay(1998)相似的条件下，证明了面板块Bootstrap方法的一阶渐近有效性。然后，我调查了面板块自举在多大程度上可以超越Driskoll和Kraay(1998)的渐近理论。

The first part of this program considers bootstrap methods for panel data regression models estimated with fixed effects. In particular, I develop a new bootstrap method for panel regression models that is robust to serial and cross sectional dependence of unknown form in the error term. I call it the panel block bootstrap method because it resamples blocks of the vector that collects the cross section units for each time period. It is the bootstrap analogue of the Driskoll and Kraay (1998) covariance estimator for spatial and serial dependent panel data models. Preliminary simulations show that the panel block bootstrap has good finite sample properties and dominates the Driskoll and Kraay (1998) robust standard errors. I prove the first order asymptotic validity of the panel block bootstrap method under conditions similar to those used by Driskoll and Kraay (1998). Then I investigate to what extent the panel block bootstrap can outperform the asymptotic theory of Driskoll and Kraay (1998).