More efficient local polynomial regression with random-effects panel data models
提出一种改进的局部多项式估计方法，通过考虑面板数据中的组内相关性，提高估计效率，并给出闭式表达式和渐近性质，模拟和实例验证了其在小样本下的优势。
We propose a modification on the local polynomial estimation procedure to account for the “within-subject” correlation presented in panel data. The proposed procedure is rather simple to compute and has a closed-form expression. We study the asymptotic bias and variance of the proposed procedure and show that it outperforms the working independence estimator uniformly up to the first order. Simulation study shows that the gains in efficiency with the proposed method in the presence of “within-subject” correlation can be significant in small samples. For illustration purposes, the procedure is applied to explore the impact of market concentration on airfare.