A Monte Carlo Evaluation of the Box-Cox Difference Transformation
用蒙特卡洛模拟评估Box-Cox差分变换在小样本（如30个观测值）中的表现，比较似然比检验与拉格朗日乘子检验，发现错误变换下R²可能更高。
The Box-Cox difference transformation permits the selection of either the first difference or percentage change form of a time series regression model. Monte Carlo evidence on the small sample properties of the transformation parameter A indicates that the difference transformation works quite well even in samples of size 30. Likelihood ratio testing is compared to an asymptotically equivalent alternative Lagrange Multiplier test. It is shown that values of R2 can often be higher for the incorrect transformation.