Bootstrap inference for fixed-effect models

Koen Jochmans and Ayden Higgins

No 22-1328, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Keywords: Bootstrap,; fixed effects; incidental parameter problem; inference, panel data (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2022-04, Revised 2023-12
New Economics Papers: this item is included in nep-dcm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Bootstrap Inference for Fixed‐Effect Models (2024)
Working Paper: Bootstrap inference for fixed-effect models (2024)
Working Paper: Bootstrap inference for fixed-effect models (2023)
Working Paper: Bootstrap inference for fixed-effect models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:126864

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