Bootstrap inference for fixed-effect models

Ayden Higgins and Koen Jochmans
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Ayden Higgins: University of Oxford

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Abstract: The maximum-likelihood estimator of nonlinear panel data models with fixed effects is asymptotically biased under rectangular-array asymptotics. The literature has devoted substantial effort to devising methods that correct for this bias as a means to salvage standard inferential procedures. The chief purpose of this paper is to show that the (recursive, parametric) bootstrap replicates the asymptotic distribution of the (uncorrected) maximum-likelihood estimator and of the likelihood-ratio statistic. This justifies the use of confidence sets and decision rules for hypothesis testing constructed via conventional bootstrap methods. No modification for the presence of bias needs to be made.

Keywords: Bootstrap; Fixed effects; Incidental-parameter problem; Inference; Panel data (search for similar items in EconPapers)
Date: 2024-03
New Economics Papers: this item is included in nep-dcm
Note: View the original document on HAL open archive server: https://hal.science/hal-04557288v1
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Citations: View citations in EconPapers (2)

Published in Econometrica, 2024, 92 (2), pp.411-427. ⟨10.3982/ECTA20712⟩

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Related works:
Journal Article: Bootstrap Inference for Fixed‐Effect Models (2024)
Working Paper: Bootstrap inference for fixed-effect models (2023)
Working Paper: Bootstrap inference for fixed-effect models (2023)
Working Paper: Bootstrap inference for fixed-effect models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04557288

DOI: 10.3982/ECTA20712

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