Semiparametric inferences for panel data models with fixed effects via nearest neighbor difference transformation
提出一种通过最近邻差分消除固定效应的部分变系数面板数据模型估计方法，并构造了函数形式检验和非参数Hausman检验，蒙特卡洛模拟验证了有限样本性能。
In this paper, we propose a simple method to estimate a partially varying-coefficient panel data model with fixed effects. By taking difference upon the nearest neighbor of the smoothing variables to remove the fixed effects, we employ the profile least squares method and local linear fitting to estimate the parametric and nonparametric parts, respectively. Moreover, a functional form specification test and a nonparametric Hausman type test are constructed and their asymptotic properties are derived. Monte Carlo simulations are conducted to examine the finite sample performance of our estimators and test statistics.