Split-panel Jackknife Estimation of Fixed-effect Models

• 成果类型: Article
• 署名作者: Dhaene, Geert; Jochmans, Koen
• 署名单位: KU Leuven; Institut d'Etudes Politiques Paris (Sciences Po)
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdv007
• 发表日期: 2015
• 页码: 991-1030
• 关键词: discrete-choice models; foreign direct-investment; time-series; unobserved heterogeneity; bias reduction; probit models; parameters; inference; INFORMATION; asymptotics

摘要

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.