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This paper addresses inference in large panel data models in the presence ofboth cross-sectional and temporal dependence of unknown form. We are interestedin making inferences that do not rely on the choice of any smoothing parameteras is the case with the often employed "HAC" estimator for the covariancematrix. To that end, we propose a cluster estimator for the asymptoticcovariance of the estimators and valid bootstrap schemes that do not requirethe selection of a bandwidth or smoothing parameter and accommodate thenonparametric nature of both temporal and cross-sectional dependence. Ourapproach is based on the observation that the spectral representation of thefixed effect panel data model is such that the errors become approximatelytemporally uncorrelated. Our proposed bootstrap schemes can be viewed as wildbootstraps in the frequency domain. We present some Monte-Carlo simulations toshed some light on the small sample performance of our inferential procedure.

Inference without smoothing for large panels with cross-sectional and temporal dependence