Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284138 
Year of Publication: 
2023
Series/Report no.: 
cemmap working paper No. CWP14/23
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 show that previously developed estimators and confidence intervals (CIs) might 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 are uniformly valid regardless of whether the factors are strong or not. 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. We use the obtained estimate to construct a bias-aware CI taking into account the remaining bias due to weak factors. In Monte Carlo experiments, we find a substantial improvement over conventional approaches when factors are weak, with little cost to estimation error when factors are strong.
Subjects: 
Panel
Fixed-effects model
Robust procedure
Inductive statistics
Interval estimation
Estimation theory
Monte Carlo simulation
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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