论文标题
固定效应模型的引导推断
Bootstrap inference for fixed-effect models
论文作者
论文摘要
具有固定效应的非线性面板数据模型的最大样品估计量是一致的，但在矩形阵列渐近造症下渐近偏置。迄今为止，文献将其努力集中在设计方法上，以纠正其偏见的最大样品估计量，以此作为挽救标准推论程序的一种手段。取而代之的是，我们表明，参数引导程序在大样本中复制了（未校正）最大似然估计器的分布。这证明了通过标准的引导百分比方法构建的置信集的使用是合理的。无需调整是否存在偏见。
The maximum-likelihood estimator of nonlinear panel data models with fixed effects is consistent but asymptotically-biased under rectangular-array asymptotics. The literature has thus far concentrated its effort on devising methods to correct the maximum-likelihood estimator for its bias as a means to salvage standard inferential procedures. Instead, we show that the parametric bootstrap replicates the distribution of the (uncorrected) maximum-likelihood estimator in large samples. This justifies the use of confidence sets constructed via standard bootstrap percentile methods. No adjustment for the presence of bias needs to be made.
