  This paper develops a novel method to estimate a latent factor model for a
large target panel with missing observations by optimally using the information
from auxiliary panel data sets. We refer to our estimator as target-PCA.
Transfer learning from auxiliary panel data allows us to deal with a large
fraction of missing observations and weak signals in the target panel. We show
that our estimator is more efficient and can consistently estimate weak
factors, which are not identifiable with conventional methods. We provide the
asymptotic inferential theory for target-PCA under very general assumptions on
the approximate factor model and missing patterns. In an empirical study of
imputing data in a mixed-frequency macroeconomic panel, we demonstrate that
target-PCA significantly outperforms all benchmark methods.
