GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT
提出一种基于一阶差分的AR(1)模型估计与推断方法，估计量几乎无有限样本偏差且对初始条件不敏感，在自回归系数通过单位根时仍保持高斯极限分布和均匀根n收敛速度。
This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform $\sqrt{n}$ rate of convergence. En route, a useful central limit theorem (CLT) for sample covariances of linear processes is given, following Phillips and Solo (1992, Annals of Statistics , 20, 971–1001). The approach also has useful extensions to dynamic panels.