Robust Estimation and Inference in Panels with Interactive Fixed Effects

• 成果类型: Article; Early Access
• 署名作者: Armstrong, Timothy B.; Weidner, Martin; Zeleneev, Andrei
• 署名单位: University of Southern California; University of Oxford; University of London; University College London
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/741623
• 发表日期: 2026
• 关键词: raise divorce rates; confidence-intervals; data models; regression; number; UNIVERSALITY; EIGENVALUE; NORM

摘要

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., a factor structure). We show that previously developed estimators and confidence intervals (CIs) are heavily biased and size distorted when some of the factors are weak. Combining the theory of minimax linear estimation with a nuclear norm bound on the error of an initial estimate of the interactive effects, we propose estimators with improved rates of convergence and uniformly valid CIs allowing for weak factors. Our method substantially outperforms conventional approaches when factors are weak, with little cost to estimation error when factors are strong.