  In this paper, we propose a simple inferential method for a wide class of
panel data models with a focus on such cases that have both serial correlation
and cross-sectional dependence. In order to establish an asymptotic theory to
support the inferential method, we develop some new and useful higher-order
expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of
simple and general conditions. We further demonstrate the usefulness of these
theoretical results by explicitly investigating a panel data model with
interactive effects which nests many traditional panel data models as special
cases. Finally, we show the superiority of our approach over several natural
competitors using extensive numerical studies.
