  Interactive fixed effects are a popular means to model unobserved
heterogeneity in panel data. Models with interactive fixed effects are well
studied in the low-dimensional case where the number of parameters to be
estimated is small. However, they are largely unexplored in the
high-dimensional case where the number of parameters is large, potentially much
larger than the sample size itself. In this paper, we develop new econometric
methods for the estimation of high-dimensional panel data models with
interactive fixed effects. Our estimator is based on similar ideas as the very
popular common correlated effects (CCE) estimator which is frequently used in
the low-dimensional case. We thus call our estimator a high-dimensional CCE
estimator. We derive theory for the estimator both in the large-T-case, where
the time series length T tends to infinity, and in the small-T-case, where T is
a fixed natural number. The theoretical analysis of the paper is complemented
by a simulation study which evaluates the finite sample performance of the
estimator.
