Split-panel Jackknife Estimation of Fixed-effect Models
针对非线性固定效应模型的最大似然估计存在 incidental-parameter 问题导致偏差大、置信区间覆盖差，本文提出一种刀切法来降低偏差并得到正确覆盖率的置信区间，适用于动态数据和多种模型。
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.