The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approaches, have been proposed to enable valid inference. This paper shows that the parametric bootstrap also enables valid inference in such models. In particular, we show that the parametric bootstrap replicates the asymptotic distribution of the maximum likelihood estimator. Therefore, it yields asymptotically unbiased estimates and confidence sets with asymptotically correct coverage. We also propose a transformation-based bootstrap confidence interval that delivers improved finite-sample performance. Simulation results support the theoretical findings. Finally, we apply the proposed method to examine technological and product market spillover effects on firms' innovation behavior.

翻译：在具有交互固定效应的非线性面板数据模型中，极大似然估计量存在偏差。已有多种偏差校正方法（如解析法和刀切法）被提出以实现有效推断。本文证明参数自举法同样能在此类模型中实现有效推断。具体而言，我们证明参数自举法能够复制极大似然估计量的渐近分布，因此可生成渐近无偏估计量及具有渐近正确覆盖率的置信集。我们还提出一种基于变换的自举置信区间，可在有限样本中实现更优表现。模拟结果支持理论发现。最后，我们将所提方法应用于检验技术溢出与产品市场溢出对企业创新行为的影响。