  We combine high-dimensional factor models with fractional integration methods
and derive models where nonstationary, potentially cointegrated data of
different persistence is modelled as a function of common fractionally
integrated factors. A two-stage estimator, that combines principal components
and the Kalman filter, is proposed. The forecast performance is studied for a
high-dimensional US macroeconomic data set, where we find that benefits from
the fractional factor models can be substantial, as they outperform univariate
autoregressions, principal components, and the factor-augmented
error-correction model.
