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

点击翻译按钮获取中文摘要

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).