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.

本文考虑具有固定效应的广义面板数据转换模型，其中假设结构函数是可加的。在我们的模型中，没有对转换函数、结构函数或特殊误差项的分布施加任何参数假设。该模型适用范围广，包括许多流行的面板数据模型作为特例。我们为结构函数提出了一个基于内核的非参数估计器。估计器具有封闭形式的解决方案并且易于实现。我们研究了估计量的渐近特性，并表明它是渐近正态分布的。蒙特卡洛模拟表明我们的新估计器在有限样本中表现良好。