CCE Estimation of Heterogeneous Panel Quantile Regression Models with Relatively Small T
提出一种适用于斜率系数异质且含交互固定效应的面板分位数回归模型的共同相关效应估计方法，通过卷积平滑推导渐近性质，并引入两步偏差校正以处理小T偏差，蒙特卡洛模拟和制造业高技能工人需求分析验证了有效性。
We consider a common correlated effects estimator for panel quantile regression models featuring heterogeneous slope coefficients and interactive fixed effects. Using convolution smoothing to facilitate the derivation of asymptotic properties, our method allows the number of time periods (T) to grow at a much slower rate than the number of cross-sectional units (N). We establish that the mean group estimator converges to a limiting normal distribution, which may have a nonzero mean in our asymptotic framework. To address biases from the smoothed objective function and a relatively small T, we introduce a two-step bias correction procedure. Monte Carlo simulations demonstrate the method’s validity in finite samples, and we apply it to analyze the sharp rise in demand for high-skilled workers in U.S. manufacturing.