Bootstrap Inference for Fixed‐Effect Models
证明递归参数Bootstrap能复制固定效应非线性面板数据模型中最大似然估计量和似然比统计量的渐近分布，从而无需偏差修正即可使用常规Bootstrap方法构建置信集和假设检验决策规则。
The maximum‐likelihood estimator of nonlinear panel data models with fixed effects is asymptotically biased under rectangular‐array asymptotics. The literature has devoted substantial effort to devising methods that correct for this bias as a means to salvage standard inferential procedures. The chief purpose of this paper is to show that the (recursive, parametric) bootstrap replicates the asymptotic distribution of the (uncorrected) maximum‐likelihood estimator and of the likelihood‐ratio statistic. This justifies the use of confidence sets and decision rules for hypothesis testing constructed via conventional bootstrap methods. No modification for the presence of bias needs to be made.