  We consider estimation and inference for a regression coefficient in panels
with interactive fixed effects (i.e., with a factor structure). We show that
previously developed estimators and confidence intervals (CIs) might be heavily
biased and size-distorted when some of the factors are weak. We propose
estimators with improved rates of convergence and bias-aware CIs that are
uniformly valid regardless of whether the factors are strong or not. Our
approach applies the theory of minimax linear estimation to form a debiased
estimate using a nuclear norm bound on the error of an initial estimate of the
interactive fixed effects. We use the obtained estimate to construct a
bias-aware CI taking into account the remaining bias due to weak factors. In
Monte Carlo experiments, we find a substantial improvement over conventional
approaches when factors are weak, with little cost to estimation error when
factors are strong.
