Bootstrap inference for fixed-effect models

Ayden Higgins and Koen Jochmans

Papers from arXiv.org

Abstract: The maximum-likelihood estimator of nonlinear panel data models with fixed effects is consistent but asymptotically-biased under rectangular-array asymptotics. The literature has thus far concentrated its effort on devising methods to correct the maximum-likelihood estimator for its bias as a means to salvage standard inferential procedures. Instead, we show that the parametric bootstrap replicates the distribution of the (uncorrected) maximum-likelihood estimator in large samples. This justifies the use of confidence sets constructed via standard bootstrap percentile methods. No adjustment for the presence of bias needs to be made.

Date: 2022-01
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (3)

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http://arxiv.org/pdf/2201.11156 Latest version (application/pdf)

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 (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2201.11156

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