https://macropaperwarehouse.com/authors/martin-weidner/Martin WeidnerHugo -- gohugo.ioen-ushttps://macropaperwarehouse.com/papers/robust-estimation-and-inference-in-panels-with-interactive-fixed-effects/https://macropaperwarehouse.com/papers/robust-estimation-and-inference-in-panels-with-interactive-fixed-effects/<p>This paper develops new estimation and inference tools for the coefficient on a covariate of interest in large panel regressions whose unobserved heterogeneity has an interactive fixed effects (factor) structure. The authors demonstrate that standard tools for this model — the least-squares estimator of Bai (2009) and the common correlated effects estimator of Pesaran (2006) — can be heavily biased and severely size-distorted when some of the factors are &ldquo;weak,&rdquo; i.e., when factor loadings and factors lack enough variation to be distinguished from noise; in their Monte Carlo designs conventional confidence intervals built on the LS estimator can have almost zero coverage. They propose a debiased estimator together with a bias-aware confidence interval that, given only an upper bound on the number of factors, remains valid uniformly over a class of data-generating processes allowing weak, strong, or nonexistent factors. The construction applies minimax linear estimation to debias a preliminary estimate of the effects matrix, using a nuclear-norm bound on that preliminary estimate&rsquo;s error, and the estimator attains a faster uniform rate of convergence than existing approaches when weak factors are allowed (reaching the parametric √(NT) rate when N and T grow at the same rate). In 5,000-replication Monte Carlo experiments and an empirical illustration calibrated to the divorce-law studies of Friedberg (1998) and Wolfers (2006), the debiased estimator substantially reduces weak-factor bias without inflating variance and performs comparably to LS when factors are strong, though the bias-aware CIs are often conservative — their oracle length is slightly less than half their actual length, bounding how much the critical value could be tightened. The method requires that the covariate of interest not itself be fully explained by a low-dimensional factor model, which rules out, for example, a treatment indicator that switches on for a subset of units in a single period.</p>