Arbeitspapier
Robust estimation and inference in panels with interactive fixed effects
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP14/23
- Klassifikation
-
Wirtschaft
- Thema
-
Panel
Fixed-effects model
Robust procedure
Inductive statistics
Interval estimation
Estimation theory
Monte Carlo simulation
Panel
Fixed-Effects-Modell
Robustes Verfahren
Induktive Statistik
Intervallschätzung
Schätztheorie
Monte-Carlo-Simulation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Armstrong, Timothy B.
Weidner, Martin
Zeleneev, Andrei
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2023
- DOI
-
doi:10.47004/wp.cem.2023.1423
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Armstrong, Timothy B.
- Weidner, Martin
- Zeleneev, Andrei
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2023
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