Timothy B. Armstrong, Martin Weidner, Andrei Zeleneev
arXiv 13 Oct 2022 · Econometrics · 5 citations (OpenAlex)
arXiv:2210.06639 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bai, J (2009) Panel data models with interactive fixed effects | 1.000 | 14 | 4 | 100% |
| 2 | Javanmard, A. and A. Montanari (2014) Confidence Intervals and Hypothesis Testing for High-Dimensional Regression | 1.000 | 5 | 4 | 100% |
| 3 | Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure | 1.000 | 5 | 3 | 100% |
| 4 | Zhu, Y (2019) How well can we learn large factor models without assuming strong factors? | 0.909 | 8 | 3 | 75% |
| 5 | Moon, H. R. and M. Weidner (2015) Linear regression for panel with unknown number of factors as interactive fixed effects | 0.874 | 12 | 5 | 67% |
| 6 | Chetverikov, D. and E. Manresa (2022) Spectral and post-spectral estimators for grouped panel data models | 0.874 | 5 | 2 | 100% |
| 7 | Wolfers, J (2006) Did unilateral divorce laws raise divorce rates? a reconciliation and new results | 0.763 | 6 | 2 | 67% |
| 8 | Armstrong, T. B., M. Kolesár, and S. Kwon (2020) Bias-Aware Inference in Regularized Regression Models self | 0.737 | 4 | 3 | 50% |
| 9 | Kim, D. and T. Oka (2014) Divorce law reforms and divorce rates in the usa: An interactive fixed-effects approach | 0.737 | 4 | 2 | 75% |
| 10 | Cox, G. F (2024) Weak identification in low-dimensional factor models with one or two factors | 0.644 | 4 | 1 | 100% |
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