NONPARAMETRIC ESTIMATION OF GENERALIZED TRANSFORMATION MODELS WITH FIXED EFFECTS
提出一种核基非参数估计量，用于估计带固定效应的广义面板数据变换模型中的结构函数，该估计量有闭式解且易于实现，蒙特卡洛模拟显示有限样本表现良好。
This paper considers a generalized panel data transformation model with fixed effects where the structural function is assumed to be additive. In our model, no parametric assumptions are imposed on the transformation function, the structural function, or the distribution of the idiosyncratic error term. The model is widely applicable and includes many popular panel data models as special cases. We propose a kernel-based nonparametric estimator for the structural function. The estimator has a closed-form solution and is easy to implement. We study the asymptotic properties of our estimator and show that it is asymptotically normally distributed. The Monte Carlo simulations demonstrate that our new estimator performs well in finite samples.