Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities
针对含个体和时间固定效应的动态非线性面板模型，提出解析偏差校正方法，使估计量渐近正态且检验统计量服从卡方分布，模拟和劳动参与率实证验证了有效性。
Abstract We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time fixed effects in both the intercept and slope. These models are subject to the incidental parameter problem, in that the limiting distribution of the point estimator is not centered at zero, and that test statistics do not follow their standard asymptotic distributions as in the absence of the fixed effects. To address the problem, we develop an analytical bias correction procedure to construct a bias-corrected likelihood. The resulting estimator follows an asymptotic normal distribution with mean zero. Moreover, likelihood-based test statistics—including likelihood-ratio, Lagrange-multiplier, and Wald tests—follow the limiting chi-squared distribution under the null hypothesis. Simulations demonstrate the effectiveness of the proposed correction method, and an empirical application on the labor force participation of single mothers underscores its practical importance.