论文标题
具有固定效果的非线性面板模型的间接推断
Indirect Inference for Nonlinear Panel Models with Fixed Effects
论文作者
论文摘要
非线性面板数据模型的固定效应估计器遇到了偶然参数问题。这在应用研究中导致了两个不良后果：（1）估计值受较大的偏见，（2）置信区间的覆盖率不正确。本文提出了一种基于仿真的偏差减少方法。该方法使用具有估计的个体效应的模型来模拟数据，并通过等同于从观察到的和模拟数据获得的固定效应估计来找到参数的值。渐近框架可提供一致性，偏置校正和渐近正态性结果。女性劳动力参与的应用和模拟说明了该方法的有限样本性能。
Fixed effect estimators of nonlinear panel data models suffer from the incidental parameter problem. This leads to two undesirable consequences in applied research: (1) point estimates are subject to large biases, and (2) confidence intervals have incorrect coverages. This paper proposes a simulation-based method for bias reduction. The method simulates data using the model with estimated individual effects, and finds values of parameters by equating fixed effect estimates obtained from observed and simulated data. The asymptotic framework provides consistency, bias correction, and asymptotic normality results. An application and simulations to female labor force participation illustrates the finite-sample performance of the method.
