Estimation and Testing in a Fixed Effects Panel Data Model With Serially Correlated Error Component Disturbances
针对固定效应面板数据模型中的AR(1)扰动，提出一种基于一阶差分消除固定效应的序列相关系数偏差校正估计量，并通过蒙特卡洛模拟和实证应用展示其优越性。
ABSTRACT This paper revisits the fixed effects panel data model with AR(1) remainder disturbances and provides a bias corrected estimator for the serial correlation coefficient based on first differencing the panel regression to get rid of the fixed effects. This bias corrected estimator builds upon the estimator proposed by Han and Phillips (2010). Asymptotic properties as well as Monte Carlo results are provided that show the better performance of this new proposed bias corrected estimator. This is extended to the unbalanced panel data case and also illustrated using the empirical application in Donohue and Levitt (2001).