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We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We demonstrate that existing estimators and confidence intervals (CIs) can 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 remain valid uniformly, regardless of factor strength. 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. Our resulting bias-aware CIs take into account the remaining bias caused by weak factors. Monte Carlo experiments show substantial improvements over conventional methods when factors are weak, with minimal costs to estimation accuracy when factors are strong.
Authors

Yale University

Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.

Andrei Zeleneev
Working Paper details
- DOI
- 10.47004/wp.cem.2024.2824
- Publisher
- Institute for Fiscal Studies
Suggested citation
T, Armstrong and M, Weidner and A, Zeleneev. (2024). Robust estimation and inference in panels with interactive fixed effects. CWP28/24. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/robust-estimation-and-inference-panels-interactive-fixed-effects-0 (accessed: 7 February 2025).
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