Higgins, Ayden and Jochmans, Koen (2024) Bootstrap inference for fixed-effect models. Econometrica. (In Press)

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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: 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: 18 Apr 2024 07:21
OAI Identifier: oai:tse-fr.eu:129027
URI: https://publications.ut-capitole.fr/id/eprint/48568
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