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Bootstrap inference for fixed-effect models

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  • Ayden Higgins
  • Koen Jochmans

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.

Suggested Citation

  • Ayden Higgins & Koen Jochmans, 2022. "Bootstrap inference for fixed-effect models," Papers 2201.11156, arXiv.org.
    • Handle: RePEc:arx:papers:2201.11156
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    Cited by:

    1. Claudia Pigini & Alessandro Pionati & Francesco Valentini, 2023. "Specification testing with grouped fixed effects," Papers 2310.01950, arXiv.org.
    2. Giuseppe Cavaliere & S'ilvia Gonc{c}alves & Morten {O}rregaard Nielsen & Edoardo Zanelli, 2022. "Bootstrap inference in the presence of bias," Papers 2208.02028, arXiv.org, revised Nov 2023.
    3. De Vos, Ignace & Stauskas, Ovidijus, 2024. "Cross-section bootstrap for CCE regressions," Journal of Econometrics, Elsevier, vol. 240(1).

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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