Backward mean transformation in unit root panel data models
研究了非平稳面板数据模型中正交向后均值变换的有效性，发现其估计量与变换最大似然估计量效率相同，且偏差修正版本接近混合最小二乘估计量。
The effectiveness of an orthogonal to backward mean transformation is investigated in the context of a non-stationary panel data model. It is shown that the corresponding estimator is as efficient as Transformed Maximum Likelihood when the autoregressive parameter is equal to unity. Furthermore, a recently introduced bias-corrected version is almost as efficient as the Pooled Least Squares estimator.