Econometrica: Mar, 2024, Volume 92, Issue 2
Bootstrap inference for fixed-effect models
https://doi.org/10.3982/ECTA20712
p. 411-427
Ayden Higgins, Koen Jochmans
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.
Supplemental Material
Supplement to "Bootstrap inference for fixed-effect models"
Ayden Higgins and Koen Jochmans
This document contains auxiliary theorems and lemmata, with proofs, that are used in the proofs in the main text.
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Supplement to "Bootstrap inference for fixed-effect models"
Ayden Higgins and Koen Jochmans
The replication package for this paper is available at https://doi.org/10.5281/zenodo.10440011. The Journal checked the data and codes included in the package for their ability to reproduce the results in the paper and approved online appendices.
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