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.

Keywords: Bias reduction; Dependent data; Incidental-parameter problem; Jackknife; Nonlinear model (search for similar items in EconPapers)
Date: 2015-02
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Citations: View citations in EconPapers (154)

Published in The Review Of Economic Studies, 2015, 82 (3), pp.991 - 1030. ⟨10.1093/restud/rdv007⟩

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Related works:
Journal Article: Split-panel Jackknife Estimation of Fixed-effect Models (2015)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2015)
Working Paper: Split-Panel Jackknife Estimation of Fixed-Effect Models (2014)
Working Paper: Split-Panel Jackknife Estimation of Fixed-Effect Models (2014)
Working Paper: Split-Panel Jackknife Estimation of Fixed-Effect Models (2014)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2010)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03392997

DOI: 10.1093/restud/rdv007

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