Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309987 
Year of Publication: 
2024
Series/Report no.: 
cemmap working paper No. CWP28/24
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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