Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form
将时间序列中未知形式异方差的自适应估计方法推广到面板数据误差成分模型，提出递归变换将随机效应和固定效应模型的误差项转化为经典误差，并改进了Breusch-Pagan检验，蒙特卡洛证据表明小样本下表现良好。
The authors show that the adaptive estimation result for the heteroskedasticity of an unknown form time-series (or cross-section) model can be generalized to the panel data error components model. The authors give recursive transformations that change the error term of a random effects model and the first differenced error term of a fixed effects model into classical errors. They also propose a modified Breusch-Pagan test for testing the random individual effects. Monte Carlo evidence suggests that the proposed estimator performs adequately in small samples. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.