  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.
