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Jochmans, Koen
and Higgins, Ayden
(2022)
Bootstrap inference for fixed-effect models.
TSE Working Paper, n. 22-1328, Toulouse
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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: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | April 2022 |
| Place of Publication: | Toulouse |
| 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) |
| Institution: | UniversitéToulouse 1 Capitole |
| Site: | UT1 |
| Date Deposited: | 11 Apr 2022 08:53 |
| Last Modified: | 04 Nov 2024 12:50 |
| OAI Identifier: | oai:tse-fr.eu:126864 |
| URI: | https://publications.ut-capitole.fr/id/eprint/45142 |
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