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Split-panel jackknife estimation of fixed-effect models

 

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Type:   Article
 
Titre:   Split-panel jackknife estimation of fixed-effect models
 
Auteur(s):   Dhaene, Geert - KU Leuven (Auteur)
Jochmans, Koen (1982-...) - Département d'économie (Auteur)
 
In:   Review of Economic Studies
 
Date de publication:   2015-02
 
Éditeur:   ÉTATS-UNIS  :  John Wiley & Sons
 
Volume:   82
 
Numéro:   3
 
Pages:   991-1030  p.
 
ISSN:   00346527
 
DOI:   10.1093/restud/rdv007
 
Mots-clés:   [en] Bias reduction, Dependent data, Incidental-parameter problem, Jackknife, Nonlinear model
 
JEL:   C13,  C14,  C22,  C23
 
Résumé:   [en] Maximum-likelihood estimation of nonlinear models with fixed effects is subject to the incidental-parameter problem. This typically implies that point estimates suffer from large bias and confidence intervals have poor coverage. This article presents a jackknife method to reduce this bias and to obtain confidence intervals that are correctly centred under rectangular-array asymptotics. The method is explicitly designed to handle dynamics in the data, and yields estimators that are straightforward to implement and can be readily applied to a range of models and estimands. We provide distribution theory for estimators of model parameters and average effects, present validity tests for the jackknife, and consider extensions to higher-order bias correction and to two-step estimation problems. An empirical illustration relating to female labour-force participation is also provided.
 
 

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