Estimation of panel model with heteroskedasticity in both idiosyncratic and individual specific errors
提出一种自适应估计方法，用核估计处理面板数据中未知的异方差性，并通过蒙特卡洛实验证明其在效率和检验大小上优于常用估计量。
In this paper we consider adaptive estimation of a panel data model with unknown heteroskedasticity in both the idiosyncratic and the individual specific random components. We use the kernel estimator for the unknown variances first and then implement the GLS estimator. We also examine the finite sample performance of the adaptive estimators and compare them with several widely used estimators via Monte Carlo experiments. We find that with a proper bandwidth, our adaptive estimator performs much better than other estimators in terms of both estimation efficiency and test size. Besides, a larger bandwidth yields better estimation efficiency and lower test size.