cemmap working paper CWP14/23
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.
Working Paper details
T, Armstrong and M, Weidner and A, Zeleneev. (2023). Robust estimation and inference in panels with interactive fixed effects. CWP14/23. London: cemmap. Available at: https://ifs.org.uk/publications/robust-estimation-and-inference-panels-interactive-fixed-effects (accessed: 3 December 2023).
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