A0397
Title: Robust estimation and inference in panels with interactive fixed effects
Authors: Timothy Armstrong - University of Southern California (United States)
Martin Weidner - University of Oxford (United Kingdom)
Andrei Zeleneev - University College London (United Kingdom) [presenting]
Abstract: Estimation and inference are considered for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). It is shown that previously developed estimators and confidence intervals (CIs) might be heavily biased and size distorted when some of the factors are weak. Estimators are proposed with improved rates of convergence and bias-aware CIs that are uniformly valid regardless of whether the factors are strong or not. The 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. The obtained estimate is used to construct a bias-aware CI, taking into account the remaining bias due to weak factors. In Monte Carlo experiments, a substantial improvement is found over conventional approaches when factors are weak, with little cost-to-estimation error when factors are strong.
