Document de travail

Bootstrap inference for fixed-effect models

Koen Jochmans et Ayden Higgins

Résumé

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.

Mots-clés

Bootstrap,; fixed effects; incidental parameter problem; inference, panel data;

Codes JEL

  • C23: Panel Data Models • Spatio-temporal Models

Remplacé par

Ayden Higgins et Koen Jochmans, « Bootstrap inference for fixed-effect models », Econometrica, vol. 92, n° 2, mars 2024, p. 411–427.

Référence

Koen Jochmans et Ayden Higgins, « Bootstrap inference for fixed-effect models », TSE Working Paper, n° 22-1328, avril 2022, révision décembre 2023.

Voir aussi

Publié dans

TSE Working Paper, n° 22-1328, avril 2022, révision décembre 2023