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: 29 Jan 2024 10:50
OAI Identifier: oai:tse-fr.eu:126864
URI: https://publications.ut-capitole.fr/id/eprint/45142
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