Review of Economic Studies Vol. 82 No. 3 2015
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.
- DOI
- 10.1093/restud/rdv007
- Volume
- 82
- Issue
- 3
- Pages
- 991-1030
- Language
- en
- Sources
- bibtex:phds-export.bib openalex crossref