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Higgins, Ayden and Jochmans, Koen
(2024)
Bootstrap inference for fixed-effect models.
Econometrica, Vol. 92 (N° 2).
pp. 411-427.
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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.
| Item Type: | Article |
|---|---|
| Language: | English |
| Date: | March 2024 |
| Refereed: | Yes |
| Place of Publication: | Chicago, Ill |
| Uncontrolled Keywords: | bootstrap, fixed effects, incidental-parameter problem, inference, panel data |
| JEL Classification: | C23 - Models with Panel Data |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 21 Feb 2024 09:54 |
| Last Modified: | 08 Nov 2024 15:13 |
| OAI Identifier: | oai:tse-fr.eu:129027 |
| URI: | https://publications.ut-capitole.fr/id/eprint/48568 |
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Bootstrap inference for fixed-effect models. (deposited 11 Apr 2022 08:53)
- Bootstrap inference for fixed-effect models. (deposited 21 Feb 2024 09:54) [Currently Displayed]
