A Simple Bootstrap Method for Panel Data Inferences

In this paper, we propose a simple dependent wild bootstrap procedure for us to establish valid inferences for a wide class of panel data models including those with interactive fixed effects. The proposed method allows for the error components having weak correlation over both dimensions, and heteroskedasticity. The asymptotic properties are established under a set of simple and general conditions, and bridge the literature on bootstrap methods and the literature of heteroskedasticity and autocorrlation consistent (HAC) approaches for panel data models. The new findings fill some gaps left by the bulk literature of the block bootstrap based panel data studies. Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies.