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Split-panel jackknife estimation of fixed-effect models

Abstract : 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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Submitted on : Thursday, October 21, 2021 - 9:22:52 PM
Last modification on : Wednesday, October 27, 2021 - 4:02:30 PM

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Geert Dhaene, Koen Jochmans. Split-panel jackknife estimation of fixed-effect models. The Review Of Economic Studies, 2015, 82 (3), pp.991 - 1030. ⟨10.1093/restud/rdv007⟩. ⟨hal-03392997⟩

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